# GitHub Copilot Fusion ($5.59/task) vs OpenCode and Roo Code + Brick: reading the Coding Agent Index v2.0 benchmarks GitHub Copilot Fusion is generally available now, and it changes what a Copilot seat actually runs and costs. Instead of locking an IDE or CLI session to a single model checkpoint, Copilot Fusion orchestrates each turn across four frontier models from Anthropic and OpenAI: **Claude Opus 5.5**, **Claude Sonnet 5.5**, **GPT-6 Astra** and **GPT-6 Sol**, billing you for whichever models executed. [Artificial Analysis measured the max setting in GitHub Copilot CLI](https://artificialanalysis.ai/articles/github-copilot-fusion-orchestrates-anthropic-and-openai-models-to-balance-performance-and-cost/) at **$5.59 per coding task** on the Coding Agent Index v2.0 (published October 28, 2026), cutting the per-task API bill in half compared to **$11.42** for Claude Code with Opus 5.5 (max). ### The question engineering leads and CTOs ask is what LLM does Copilot use under the hood, and how much can we save by running the same multi-model routing pattern on open weights in Europe? Below we break down the four models and three orchestration patterns inside Copilot Fusion, examine the sub-benchmark split in the Coding Agent Index v2.0, calculate what an equivalent 7.2M-token task costs on our flat [Core token pool](https://regolo.ai/pricing/) (€0.47 at full utilization, €0.14 during the 3-month promo) or on pure pay-as-you-go (~€1.85 per task), and show how to configure [OpenCode and Brick](https://regolo.ai/opencode-brick-for-multi-agent-coding-and-optimize-costs-up-to-80/) or [Roo Code](https://regolo.ai/roo-code-ai-powered-autonomous-coding-in-vscode/) on our European GPUs with certified Zero Data Retention. BENCHMARK · ARTIFICIAL ANALYSIS CODING AGENT INDEX V2.0 · OCTOBER 2026 ### Copilot Fusion halves the token bill to $5.59 per task. Routing open weights on our EU pool brings a 7.2M-token task down to €0.47. Cost per 7.2M-token coding task, token volume, Coding Agent Index v2.0 scores and open-weight output speeds measured by Artificial Analysis. Our European open-weight routing stack (Core pool & Brick PAYG) and routed highlights Proprietary cloud baselines (Copilot Fusion, Devin Fusion and Claude Code Opus 5.5) Cost per 7.2M-token coding task (lower is better) Core (×1)€0.47 Core (Brick)€0.73 Copilot Fusion$5.59 Devin Fusion$8.00 Claude Code$11.42 Total tokens per task, millions (lower is better) Copilot Fusion7.2M Devin Fusion10.3M Claude Code14.4M Coding Agent Index v2.0 & sub-benchmarks (higher is better) CF Term-Bench71% Claude Code62 pts Copilot Fusion60 pts Top Open Wt.48 pts Open-weight output speed, tok/s (Artificial Analysis) gpt-oss-120b149 tok/s mistral-sm-4140 tok/s glm5.284 tok/s qwen3.8-27b44 tok/s **€0.47 to €0.73**7.2M-token task on our €39 Core pool (×1 vs ×1.55 Brick mix) or €4.37 on PAYG (down from €20.53 unrouted GLM-5.2) **7.2M vs 14.4M**tokens per task: routing cuts volume in half compared to Claude Code with Opus 5.5 max **71% vs 51%**Copilot Fusion leads Terminal-Bench 4.0 (71%) and scores 51% on FrontierCode v1.1 Sources: Artificial Analysis Coding Agent Index v2.0 and coding agent benchmark pages, published October 28, 2026 (FrontierCode v1.1, 100 tasks; Terminal-Bench 4.0, 66 tasks; SWE-Atlas-QnA-AA, 124 tasks, three attempts per task). Output speeds from Artificial Analysis model pages, retrieved November 6, 2026. GitHub Copilot Fusion changelog, October 28, 2026. ## What LLM does Copilot use in 2026? GitHub Copilot no longer relies on a single static model. With **Copilot Fusion**, GitHub runs a runtime model orchestration layer that evaluates every prompt and dispatches it across four proprietary models: **Claude Opus 5.5** and **Claude Sonnet 5.5** from Anthropic, plus **GPT-6 Astra** and **GPT-6 Sol** from OpenAI. [The GitHub changelog from October 28, 2026](https://github.blog/changelog/2026-10-28-route-and-orchestrate-models-with-github-copilot-fusion/) confirms that a single coding task can invoke an Anthropic model, an OpenAI model, or both inside the same multi-step run. Instead of picking a model checkpoint in the dropdown, developers select one of three effort levels. **Fusion Standard** favors fast, versatile models for everyday autocomplete and quick edits. **Fusion High** shifts weight toward deeper reasoning for multi-file changes. **Fusion Max** prioritizes the largest frontier models for complex debugging and repository-wide tasks. Artificial Analysis tested Fusion Max inside the GitHub Copilot CLI, so the $5.59 per task figure represents the highest-capability setting. ### The three execution patterns behind every Fusion task Under the hood, GitHub documents three orchestration patterns that Fusion chooses per turn. **Single** sends the turn to one model directly when the task complexity is clear. **Cascade** lets an efficient model (such as Sonnet 5.5 or GPT-6 Sol) draft the response, runs an automated quality check, and escalates to Opus 5.5 or GPT-6 Astra only if the draft falls short. **Critique** pairs two models so that a first model drafts a patch, a second model reviews it for edge cases, and the first model revises the code before returning it to the IDE. Cascade is the mechanism that cuts token spend in half, because routine file reads and simple edits pass the quality check on the first try without burning frontier tokens. ARCHITECTURE COMPARISON · PROPRIETARY CLOUD ROUTER VS OPEN-WEIGHT EU ROUTER### How Copilot Fusion and Brick route a 25-turn coding session Both systems stop wasting frontier reasoning tokens on file searches and log inspection. The difference is which models sit behind the router, how they are billed, and where your code travels. Proprietary US Cloud · Metered per modelGitHub Copilot Fusion (Standard / High / Max) 1. Developer prompt & effort policy VS Code or GitHub Copilot CLI Admin model policies filter which Anthropic and OpenAI endpoints are allowed for the organization. ↓ 2. Runtime orchestration pattern Single · Cascade · Critique **Cascade** drafts on `Claude Sonnet 5.5` or `GPT-6 Sol` and escalates to `Claude Opus 5.5` or `GPT-6 Astra` only when the quality gate fails. ↓ **Outcome on Coding Agent Index v2.0 (Max):** 60 composite score (71% Terminal-Bench 4.0, 51% FrontierCode v1.1) · **7.2M tokens** · **$5.59 per task** · Prompts processed across US cloud endpoints. Sovereign EU GPUs · Flat Pool or PAYG · ZDROpenCode / Roo Code + Brick (`brick-v1-beta`) 1. Native OpenCode v2 provider or Roo Code IDE OpenCode v2 (`/connect` → Regolo AI) or Roo Code Select `regolo-ai/brick-v1-beta` natively in OpenCode v2 (or `https://api.regolo.ai/v1` in Roo Code). The harness manages compaction while Brick classifies each turn in ~150ms. ↓ 2. Open-source MoM routing (regolo-ai/brick-SR1) 3 open-weight tiers on European GPUs • **Discovery (~75% of turns):** `gpt-oss-120b` (149 tok/s) or `mistral-small-4` (140 tok/s) for grep, file reads and JSON schemas. • **Implementation (~15%):** `qwen3.8-27b` (256k ctx, 61.7% SWE-bench Pro) for single-file patches and unit tests. • **Architecture (~10%):** `glm5.2` (1M ctx, MIT) for multi-file refactoring and deep debugging. ↓ **Outcome for a 7.2M-token routed task:** **€0.47 per task** (×1) or **€0.73 per task** (×1.55 weighted quota mix; €0.14 to €0.22 on 3-mo promo) · **€4.37 on PAYG** (vs €20.53 unrouted GLM-5.2) · EU ZDR. Sources: GitHub Copilot Fusion changelog (Oct 28, 2026), Artificial Analysis Coding Agent Index v2.0 (Oct 28, 2026), and Brick open-source router repository (github.com/regolo-ai/brick-SR1). ### What administrators control and how Fusion is billed Enterprise and organization administrators can audit which models Fusion selects, set the effective policy state for each vendor model, disable a specific model inside Fusion without removing it from manual selection, and assign Fusion at Standard, High or Max as the default for their seats. Billing follows the underlying models rather than a flat bundle: GitHub charges each model at its standard rate, with a temporary 30% discount on model costs for paid plans during the launch window. Because GitHub has not published an expiration date for that promotion, engineering budgets should model the unsubsidized rate. One naming detail to keep straight when reading the charts: **Devin Fusion** is a separate multi-model product built by Cognition, not a GitHub Copilot mode. In the Artificial Analysis evaluation, Devin Fusion paired Claude Opus 5.5 (max) with GPT-6 Luna (high). SOVEREIGN EUROPEAN INFERENCE Run GLM-5.2, Qwen3.8-27B and Mistral Small 4 in Europe with Zero Data Retention Get 600 million monthly tokens on our Core plan at a flat €39 per month (€11.70 per month for the first 3 months), hosted entirely in our European datacenters. Point OpenCode or Roo Code to our OpenAI-compatible endpoint in one line of configuration. [See our pricing and model catalogue →](https://regolo.ai/pricing/) Free trial included · 100% green energy · ✓ EU green datacenters ✓ Certified ZDR ## What the Coding Agent Index v2.0 measured The Artificial Analysis Coding Agent Index v2.0 is an equal-weight composite of three benchmarks: **FrontierCode v1.1** (100 real software engineering tasks graded by blocking and non-blocking checks), **Terminal-Bench 4.0** (66 interactive command-line workflows scored pass or fail), and **SWE-Atlas-QnA-AA** (124 repository architecture and codebase navigation questions). Every task runs three times and the reported score is the three-run average. Looking behind the composite score reveals an important split. Copilot Fusion takes first place on Terminal-Bench 4.0 at **71%**, scores **57%** on SWE-Atlas-QnA-AA, and lands third on FrontierCode v1.1 at **51%**, behind Claude Code with Opus 5.5 max (**56%**) and Devin Fusion (**54%**). Terminal loops benefit immediately from fast Cascade routing because most shell steps are short command verifications, whereas deep repository patches on FrontierCode v1.1 still reward keeping a heavy planner active across every file edit. SUB-BENCHMARK SPLIT · AA CODING AGENT INDEX V2.0 (OCT 28, 2026)### Where Copilot Fusion wins and where single-model Opus 5.5 leads Terminal-Bench 4.0 (66 tasks), SWE-Atlas-QnA-AA (124 tasks) and FrontierCode v1.1 (100 tasks) CF Term 4.071% (#1) CF SWE-Atlas57% Opus FCode156% (#1) Devin FCode154% (#2) CF FCode151% (#3) Copilot Fusion (max) leads Terminal-Bench 4.0 at 71% while trailing single-model Claude Opus 5.5 max by 5 points on FrontierCode v1.1 (51% vs 56%) at half the token cost ($5.59 vs $11.42). Source: Artificial Analysis Coding Agent Index v2.0 (Oct 28, 2026). Keep one accounting distinction in mind when reading the cost column below. Cost per task in the index measures raw pay-per-token API consumption for the models invoked during the benchmark run. It does not include the monthly Copilot seat fee, cloud sandbox hosting, or developer supervision. Coding Agent Index v2.0: full sub-benchmark and cost breakdown Artificial Analysis Coding Agent Index v2.0, published October 28, 2026 (three attempts per task) | Agent setup | Index scorecomposite (0 to 100) | Terminal-Bench 4.066 CLI tasks | FrontierCode v1.1100 repo tasks | SWE-Atlas-QnA124 Q&A tasks | Tokens & API costper completed task | |---|---|---|---|---|---| | Copilot Fusionmax, GitHub Copilot CLI | 60 | 71% (#1) | 51% | 57% | 7.2M · $5.59 | | Devin FusionOpus 5.5 max + GPT-6 Luna high | 62 | Published in composite | 54% | Published in composite | 10.3M · $8.00 | | Claude CodeClaude Opus 5.5 max | 62 | Published in composite | 56% (#1) | Published in composite | 14.4M · $11.42 | | Top open-weight baselineSingle open model on AA Index | 48 | 41% to 46% range | 28% (top open weight) | Published in composite | €0.47 (×1) / €0.73 (Brick) CoreFor a 7.2M-token Brick run | Composite score weights FrontierCode v1.1, Terminal-Bench 4.0 and SWE-Atlas-QnA-AA equally across three attempts per task. Artificial Analysis publishes the full interactive leaderboard on its [coding agent benchmark page](https://artificialanalysis.ai/agents/coding-agents?routers=true). ## What a 7.2M-token coding task costs on our flat pool vs pay-as-you-go Replacing per-token US cloud billing with a flat European token pool changes the unit economics of agentic coding. Our [Core plan](https://regolo.ai/pricing/) provides up to **600M tokens per month for a flat €39** (with a 20M daily token cap, and **70% off at €11.70 per month for the first three months**). A 7.2M-token coding task, matching the exact token volume Artificial Analysis measured for Copilot Fusion, consumes **1.2% of a monthly Core pool**, or about **83 full benchmark tasks per month**. ### Quota usage multipliers, pool utilization and pay-as-you-go math On our subscription plans, every model carries a quota usage multiplier that determines how rapidly a request draws from your allowance: `brick-v1-beta`, `gpt-oss-20b` and `qwen3.5-9b` run at **×0.5**, `gpt-oss-120b` and `mistral-small-4-119b` run at **×1**, `qwen3.8-27b` runs at **×2**, and `glm5.2` runs at **×5**. When you route through `brick-v1-beta`, Brick classifies the prompt (at ×0.5) and your account is billed at the multiplier or token rate of the target model that executes the turn. That multiplier gap is the strongest financial reason to use a router on our plans. If you lock an agent to `glm5.2` (×5) on every turn, a single 7.2M-token task consumes **36M quota tokens**, exceeding the entire 20M daily cap of a Core plan in one run and absorbing **€2.34** of monthly pool value (only 16 tasks per month on Core). When Brick routes 75% of turns to `gpt-oss-120b` or `mistral-small-4-119b` (×1), 15% to `qwen3.8-27b` (×2) and 10% to `glm5.2` (×5), the weighted multiplier drops from ×5 to **×1.55** (11.16M quota tokens per task). At 100% Core pool utilization (€0.065 per 1M quota tokens), a 7.2M-token task costs **€0.47 at ×1 face value** (83 tasks/month) and **€0.73 with the ×1.55 Brick mix** (~53 tasks/month, or **€0.14 to €0.22 per task** during the €11.70 three-month promo). At 50% pool utilization, those figures double to €0.94 (×1) and €1.45 (×1.55 Brick mix) while your monthly invoice stays fixed at €39. The same routing effect shows up on pure pay-as-you-go (88% input tokens = 6.34M, 12% output tokens = 0.86M per 7.2M-token task). Running 100% of turns on `glm5.2` (€2.30 input / €6.90 output per 1M, or €2.852/1M blended) costs **€20.53 per task**. Routing 75% of turns (5.40M tokens) to `gpt-oss-120b` or `mistral-small-4-119b` (€0.20 / €0.70, €1.40 total), 15% (1.08M tokens) to `qwen3.8-27b` (€0.50 / €3.40, €0.92 total) and 10% (0.72M tokens) to `glm5.2` (€2.05 total) cuts the pay-as-you-go bill by 79% to **€4.37 per task** (or **€1.87 per task** on the ×1 fast tier alone). UNIT ECONOMICS · COST PER 7.2M-TOKEN CODING TASK### Cost per 7.2M-token coding task across plans and cloud routers Comparing our flat Core pool (×1 and ×1.55 Brick mix) and PAYG against metered US frontier agents Core (×1)€0.47/task Core (Brick)€0.73/task Brick PAYG€4.37/task Copilot Fus.$5.59/task Devin Fusion$8.00/task Claude Code$11.42/task Core (×1) is €0.47/task at 100% pool utilization (€0.14 on 3-month promo). Core (Brick) applies the ×1.55 weighted quota multiplier (75% ×1, 15% ×2, 10% ×5 = €0.73/task, or €0.22 on promo). Brick PAYG (€4.37/task) cuts unrouted GLM-5.2 PAYG (€20.53/task) by 79%. ### How many agent tasks a development team actually runs per month Sizing the right pool for a team is straightforward once you count tasks per developer. One developer running 50 routed multi-tier tasks a month consumes ~558M quota tokens (at ×1.55), fitting inside a single **Core** plan (€39/month, 600M quota tokens). Two developers running 65 multi-tier tasks each fit inside **Boost** (1.5B quota tokens for €89/month, covering 134 Brick-routed tasks or 208 ×1 tasks). An eight-developer squad running 65 multi-tier tasks each fits inside **Scale** (6B quota tokens for €319/month, covering 537 Brick-routed tasks or 833 ×1 tasks). Monthly token pool capacity vs metered Copilot Fusion spend Our flat monthly plans measured against a 7.2M-token coding benchmark task (×1 face value vs ×1.55 Brick mix) | Plan | Monthly pool& daily cap | Monthly pricestandard (3-mo promo) | Tasks covered×1 tier / ×1.55 Brick mix | Cost per task (100% use)×1 tier / ×1.55 Brick mix | Same tasks on Fusionat $5.59 per task | |---|---|---|---|---|---| | Core1 to 2 developers | 600M20M / day cap | €39/mo (€11.70 promo) | 83 / 53 tasks | €0.47 / €0.73€0.14 / €0.22 on promo | $296 to $464 | | Boost2 to 4 developers | 1.5B50M / day cap | €89/mo | 208 / 134 tasks | €0.43 / €0.66 | $749 to $1,163 | | Pro4 to 6 developers | 3B100M / day cap | €169/mo | 416 / 268 tasks | €0.41 / €0.63 | $1,498 to $2,325 | | Scale8+ developer squad | 6B200M / day cap | €319/mo | 833 / 537 tasks | €0.38 / €0.59 | $3,002 to $4,656 | Tasks covered shows both ×1 models (`gpt-oss-120b`, `mistral-small-4-119b`: 7.2M quota per task) and the 75/15/10 Brick mix (×1.55 weighted multiplier: 11.16M quota per task, vs 36M quota if running `glm5.2` at ×5 on every turn). ## How to reproduce the routing with OpenCode, Roo Code and Brick While GitHub just introduced multi-model routing in Copilot Fusion, we have been routing requests across every model in our European pool for a long time with **Brick** (`brick-v1-beta`), our open-source Mixture-of-Models (MoM) semantic routing gateway. It inspects the capability requirements and complexity of each incoming prompt in about 150ms and routes the request to the most cost-effective model in the pool. Brick is a routing gateway, not a standalone generative LLM or a context compressor: your coding harness (OpenCode or Roo Code) handles context window compaction, while Brick selects which backend model answers the turn. Because the [Brick router is open source on GitHub](https://github.com/regolo-ai/brick-SR1), your security team can inspect the exact classification logic before connecting a private codebase. ### 1. Configuring OpenCode v2 with the native Regolo AI provider In OpenCode v2, **Regolo AI** (provider ID `regolo-ai`) is included directly in the built-in provider catalog, so you do not need to write a custom OpenAI-compatible provider block. Run `/connect` in the OpenCode TUI, select **Regolo AI**, and paste your API key (or export `REGOLO_API_KEY` in your shell). Then set `regolo-ai/brick-v1-beta` as your default model in `opencode.jsonc` and optionally pin specific tiers to OpenCode's built-in `explore`, `plan` and `build` agents: ``` // ~/.config/opencode/opencode.jsonc (OpenCode v2 native config) { "$schema": "https://opencode.ai/config.json", "model": "regolo-ai/brick-v1-beta", "agents": { "build": { "model": "regolo-ai/brick-v1-beta" }, "explore": { "model": "regolo-ai/gpt-oss-120b" }, "plan": { "model": "regolo-ai/glm5.2#high" } } } ``` ``` // ~/.config/opencode/agents/reviewer.md (OpenCode v2 custom subagent) --- description: Reviews code diffs and unit tests on Qwen3.8-27B mode: subagent model: regolo-ai/qwen3.8-27b permissions: - action: edit resource: "*" effect: deny --- Review the current changes for regressions and missing edge cases. ``` All catalog models under `regolo-ai/` support variants out of the box in OpenCode v2, including `regolo-ai/glm5.2#high` and `regolo-ai/glm5.2#max` for deep architectural planning and `regolo-ai/gpt-oss-120b#low` for fast repository scans. Our [OpenCode and Brick multi-agent walkthrough](https://regolo.ai/opencode-brick-for-multi-agent-coding-and-optimize-costs-up-to-80/) documents how subagent delegation cuts multi-agent token spend by up to 80%. ### 2. Configuring Roo Code in VS Code In Roo Code, you can either point your default profile directly to `brick-v1-beta` for automatic per-turn routing, or bind each Roo Code mode (*Ask*, *Code*, *Architect*) to the matching open-weight tier on `https://api.regolo.ai/v1`. Our [Roo Code guide for private EU inference](https://regolo.ai/roo-code-ai-powered-autonomous-coding-in-vscode/) walks through every provider field in VS Code. ``` // Roo Code provider settings (VS Code settings.json / profile export) { "roo-cline.apiProvider": "openai", "roo-cline.openAiBaseUrl": "https://api.regolo.ai/v1", "roo-cline.openAiModelId": "brick-v1-beta", "roo-cline.modeApiConfigs": { "ask": "regolo-fast-gpt-oss-120b", "code": "regolo-workhorse-qwen3-8-27b", "architect": "regolo-reasoning-glm-5-2" } } ``` If your team works across large monorepos, combine this routing setup with [persistent Git-backed codebase memory in ai-memory](https://regolo.ai/getting-started-ai-memory-regolo-persistent-codebase-memory/) and our [context-mode and headroom guide](https://regolo.ai/getting-started-context-mode-headroom-regolo/) so the agent does not re-read the same directory tree at the start of every session. You can also compare the day-to-day operational differences between per-seat assistants and custom endpoints in our [GitHub Copilot vs custom LLM assistant breakdown](https://regolo.ai/github-copilot-alternative-copilot-vs-custom-llm-assistant-cheaper-and-faster/). ## Speed, licensing and European data residency Output speed determines how responsive an agent feels in the terminal, because the harness blocks on model generation at every step. The table below lists the output speeds Artificial Analysis measured on standardized hardware alongside our live [model catalogue](https://regolo.ai/models/) pricing and context limits. Our open-weight coding tier: speed, context, license and PAYG rates Output speed from Artificial Analysis (retrieved November 6, 2026), rates from our live European API | Model ID | Routing role | Output speedArtificial Analysis | Contextwindow | Pool mult.& license | Pay-as-you-goinput / output per 1M | |---|---|---|---|---|---| | [gpt-oss-120b](https://artificialanalysis.ai/models/gpt-oss-120b)5.1B active MoE | Fast discovery, grep & JSON | 149 tok/s | 131k | **×1** · Apache 2.0 | €0.20 / €0.70 | | [mistral-small-4](https://artificialanalysis.ai/models/mistral-small-4)Fast European generalist | Repo search & log triage | 140 tok/s | 256k | **×1** · Apache 2.0 | €0.20 / €0.70 | | [qwen3.8-27b](https://artificialanalysis.ai/models/qwen3-8-27b)Dense coding & vision | Single-file patches & unit tests | 44 tok/s | 256k | **×2** · Apache 2.0 | €0.50 / €3.40 | | [glm5.2](https://artificialanalysis.ai/models/glm-5-2)744B MoE architect | Multi-file planning & hard bugs | 84 tok/s | 1M | **×5** · MIT | €2.30 / €6.90 | All four models are included in our flat Core, Boost, Pro and Scale monthly pools, or available on pay-as-you-go alongside `brick-v1-beta` (€0.10 input / €0.20 output per 1M tokens). Open licensing and data residency separate this stack from Copilot Fusion. Because `gpt-oss-120b`, `mistral-small-4` and `qwen3.8-27b` ship under Apache 2.0 and `glm5.2` ships under MIT, you are never locked into a proprietary checkpoint: you can call them through our shared pool, deploy them on dedicated Custom Models GPUs, or run the exact same weights on-premise. Every request to our API executes in RAM inside our European datacenters under certified Zero Data Retention and ISO/IEC 27001, 27017 and 27018 compliance, documented on our [Zero Data Retention page](https://regolo.ai/zero-data-retention/). With Copilot Fusion, prompts traverse Microsoft, Anthropic and OpenAI cloud infrastructure under US jurisdiction. ## Which setup fits your team? DECISION MAP · MATCH YOUR WORKLOAD TO A MODEL TIER### Which model tier should handle the turn? Brick inspects each prompt and routes it to the most cost-effective model in our European pool. Here is how the three tiers split a coding session. 1. Type of turn 2. Model Brick routes to 3. Best fit for Discovery & inspection (~75% of turns) File search, grep, log reading, JSON schemas High-frequency mechanical steps in an agent loop → Fast tier gpt-oss-120b / mistral-small-4 140 to 149 tok/s Apache 2.0 €0.20/0.70 per 1M → Best for High-frequency IDE and CLI loops Eliminates frontier token waste on repo exploration Implementation & review (~15% of turns) Single-file patches, unit tests, code review Requires reliable tool calling and 256k working context → Workhorse tier qwen3.8-27b 61.7% SWE-bench Pro 256k ctx Apache 2.0 → Best for Product teams in VS Code & OpenCode 46.0% on DeepSWE v1.1 with preserved thinking enabled Architecture & hard debugging (~10% of turns) Multi-file refactors, planning, long-context audits Complex synthesis across modules and strict policy checks → Reasoning tier glm5.2 84 tok/s 1M ctx 99.1% tau2-bench → Best for Platform teams with large monorepos Full 1M context window under European Zero Data Retention Brick adds ~150ms per request for complexity classification. You can also pin `glm5.2` or `qwen3.8-27b` directly on specific subagents in OpenCode and Roo Code. The trade-off between proprietary cloud routing and our open-weight European stack comes down to three numbers. On the Artificial Analysis Coding Agent Index v2.0, top open-weight models score **48** compared to **60** for Copilot Fusion and **62** for Claude Code with Opus 5.5 max. On real repository benchmarks, [Qwen3.8-27B](https://regolo.ai/qwen3-8-27b-benchmarks-every-test-where-alibabas-27b-model-beats-claude-opus-4-6-max/) reaches **61.7% on SWE-bench Pro** (and **46.0% on DeepSWE v1.1** when configured with preserved thinking, as detailed in our [harness engineering guide for Qwen3.8-27B](https://regolo.ai/harness-engineering-for-qwen3-8-27b-a-technical-guide-to-pi-opencode-and-kilo-code/)), while **GLM-5.2** supplies a **1M-token context window** and **99.1% on tau2-bench** for multi-file planning. If your team wants a managed US SaaS orchestrator inside GitHub and accepts metered per-model billing around $5.59 per benchmark task, Copilot Fusion at Max is a major step forward from single-model Opus 5.5 ($11.42 per task). If you build software in Europe, need strict Zero Data Retention in RAM without US CLOUD Act exposure, or want predictable monthly unit economics (€39/month flat for ~83 tasks on Core, or ~€1.85 per task on pay-as-you-go), routing OpenCode or Roo Code through `brick-v1-beta` to `gpt-oss-120b`, `mistral-small-4`, `qwen3.8-27b` and `glm5.2` keeps 100% of your coding workflow inside our European datacenters. Browse our [comparison directory](https://regolo.ai/compare/) for head-to-head evaluations across every model we serve. ZERO DATA RETENTION Get engineering notes and model releases in your inbox. Sovereign AI inference, OpenAI-compatible, EU-hosted. Benchmarks, new models (Mistral, Qwen, GLM), and cost insights. One email, no spam. ✓ Thank you for subscribing! Please check your inbox to confirm your subscription. Subscribe Free I accept the [Privacy Policy](https://regolo.ai/privacy-policy/) \* 100% European infrastructure · Zero spam · Unsubscribe anytime. ## Frequently asked questions **What LLM does GitHub Copilot use in late 2026?** With GitHub Copilot Fusion (released October 28, 2026), Copilot does not lock you to a single LLM. It routes each turn across four frontier models: Anthropic's Claude Opus 5.5 and Claude Sonnet 5.5, and OpenAI's GPT-6 Astra and GPT-6 Sol. Developers pick an effort level (Standard, High or Max) rather than a specific checkpoint, and GitHub bills for whichever underlying models ran. **How much does GitHub Copilot Fusion cost per task compared to Claude Code?** On the Artificial Analysis Coding Agent Index v2.0 (October 28, 2026), GitHub Copilot Fusion at its Max setting cost $5.59 per task and consumed 7.2M tokens, compared to $11.42 per task and 14.4M tokens for Claude Code running Claude Opus 5.5 (max), and $8.00 per task (10.3M tokens) for Cognition's Devin Fusion. **Why does Copilot Fusion score 71% on Terminal-Bench 4.0 but 51% on FrontierCode v1.1?** Terminal-Bench 4.0 (66 tasks) tests interactive command-line execution and error recovery, where Fusion's Cascade pattern drafts fast commands on lighter models and takes first place at 71%. FrontierCode v1.1 (100 repository tasks) grades complex multi-file pull requests, where keeping Claude Opus 5.5 max active on every turn scores 56% against 51% for Copilot Fusion. **How can I build an open-weight Copilot Fusion alternative in OpenCode or Roo Code?** In OpenCode v2, Regolo AI is a built-in catalog provider: run `/connect`, select Regolo AI, and set `"model": "regolo-ai/brick-v1-beta"` in `opencode.jsonc` (or point Roo Code to `https://api.regolo.ai/v1`). Our open-source Brick semantic router classifies each prompt in ~150ms, sending file search and grep turns (~75% of traffic) to `gpt-oss-120b` (149 tok/s) or `mistral-small-4` (140 tok/s), single-file edits to `qwen3.8-27b` (256k context), and multi-file architecture turns to `glm5.2` (1M context) on European GPUs. **What does a 7.2M-token coding agent task cost on Regolo?** Our flat Core plan costs €39 per month for 600M tokens (€11.70 per month for the first three months), so a 7.2M-token task uses 1.2% of the pool (about 83 tasks per month). That equals €0.47 per task at 100% pool utilization (€0.14 per task during the 3-month promo, or €0.94 at 50% utilization). On pure pay-as-you-go without a subscription, a 75/15/10 Brick-routed task costs roughly €1.85. **How do open-weight coding models compare to Copilot Fusion on accuracy and privacy?** On the Artificial Analysis Coding Agent Index v2.0, leading open-weight models score 48 against 60 for Copilot Fusion, while Qwen3.8-27B scores 61.7% on SWE-bench Pro (and 46.0% on DeepSWE v1.1 with preserved thinking) and GLM-5.2 reaches 99.1% on tau2-bench with a 1M context window. In exchange for that 12-point composite difference, running your entire workflow on our European stack cuts per-task spend to €0.47 on Core (€1.85 PAYG) and keeps all source code in RAM inside EU datacenters under certified Zero Data Retention (ISO/IEC 27001, 27017 and 27018). PRODUCTION-READY EU INFERENCE · ZERO DATA RETENTION OpenAI-Compatible API Ship Private AI. Not Infrastructure. You have the architecture and the open-source workflow. Now give it a sovereign European inference layer built for production: fast access to **GLM-5.2, Qwen3.5, Mistral, Llama, DeepSeek**, and **Brick Complexity Pro** with zero data retention and no new SDK to learn. DROP-IN ENDPOINT `export OPENAI_BASE_URL="https://api.regolo.ai/v1"` Keep your existing SDK & agent setup [ 30-DAY FREE TRIAL Start your 30-day free trial → Build, test, and deploy with no infrastructure to maintain. Core plans from €11.70/mo (70% off first 3 months). ](https://regolo.ai/pricing/) [ OPEN-SOURCE CODE Clone the GitHub repository → Get the full implementation, configuration scripts, evaluation benchmarks, and ready-to-run templates. ](https://github.com/regolo-ai/tutorials/) [ BUILDER COMMUNITY Join the Regolo Discord → Meet engineers building with coding agents, private RAG, local LLMs, and production open-weight models. ](https://discord.gg/bqGrVJHeF) [ ARCHITECTURE REVIEW Talk to an AI Infrastructure Engineer → Scaling beyond a proof of concept or running regulated EU workloads? Get a tailored architecture and commercial plan. ](https://regolo.ai/contacts/) **Private AI should not require a private data center.** Regolo gives your team an EU-native path from local experimentation to production-grade inference. [API Docs](https://docs.regolo.ai) · [GitHub](https://github.com/regolo-ai/tutorials/) · [Discord](https://discord.gg/bqGrVJHeF) · [X (@regolo\_ai)](https://x.com/regolo_ai) · [Reddit](https://www.reddit.com/r/regolo_ai/) · [Contact](https://regolo.ai/contacts/) Built by the Regolo team · 100% EU Sovereign GPU