# DeepSeek V4 Flash vs Qwen3.8-Flash-Next vs GLM-5.3-Flash: the real leader in quality-to-price in 2026

In mid-to-late 2026 the "Flash" tier of open-weight models has become the practical workhorse for production systems: these are not the absolute frontier flagships (those still carry higher latency and much higher cost). They are the models teams actually route the majority of traffic to: high volume, strong enough capability, and dramatically better economics.

This article compares the three current leaders of that tier — **DeepSeek V4 Flash (0731)**, **Qwen3.8-Flash-Next** (served in production as Qwen3.8-Flash), and **GLM-5.3-Flash** — using official model cards, Artificial Analysis independent measurements, provider pricing pages, and third-party reporting as of early September 2026. Every benchmark number below is sourced and dated; where a figure is vendor-reported rather than independently reproduced, we say so.

## Model snapshot

| Model | Developer | Architecture | Active Params | Context | Release | License |
|---|---|---|---|---|---|---|
| **DeepSeek V4 Flash** | DeepSeek | 284B MoE | ~13B | 1M (up to ~1.31M on some providers) | Apr 2026; 0731 refresh Jul 31, 2026 | Open weights (MIT) |
| **Qwen3.8-Flash-Next** | Alibaba (Qwen) | 125B MoE + 51B n-gram embedding layer (~177B total) | ~6B | 262K native → 1M (YaRN) | Aug 26, 2026 | Open weights (Qwen Community) |
| **GLM-5.3-Flash** | Z.ai (Zhipu) | 320B MoE, hybrid linear + sparse attention | ~18B | 1,048,576 tokens | Aug 26, 2026 | Open weights (MIT) |

All three support tool use / function calling and reasoning modes (thinking is on by default in Qwen3.8-Flash-Next and DeepSeek V4 Flash). Qwen3.8-Flash-Next and GLM-5.3-Flash are natively multimodal — GLM accepts image **and video** input; DeepSeek V4 Flash is text-first in its main checkpoint (vision variants exist separately).

  SOVEREIGN EUROPEAN INFERENCE 

###  Run DeepSeek, Qwen, and GLM in Europe with Zero Data Retention 

 Get 600 Million tokens on the Regolo Core plan (€39/mo flat, ~€0.065/1M). Switch endpoints in 1 line of code with full OpenAI SDK compatibility on 100% green datacenters.

 [ Start 30-day free trial (no card required) → ](https://regolo.ai/pricing/?utm_source=blog&utm_medium=bento_cta&utm_campaign=deepseek-flash-mid)  Free credits included · 100% green energy  

  ✓ 100% EU Green Datacenters   ✓ Certified ZDR  

 

 

## Published Benchmark Scores

This is the table the original version of this article was missing. Scores are as published by each vendor's model card unless marked **\[AA\]**, which denotes Artificial Analysis's independent measurement. Harnesses, temperatures, and context limits differ per vendor — treat cross-model gaps of a few points as directional, not definitive.

**How to read this table:** GLM-5.3-Flash posts the strongest agentic-coding number of the tier (DeepSWE 63.4, vs GLM-5.2's 46.2) and the top independent intelligence score. Qwen3.8-Flash-Next sweeps the language, reasoning, and office-agent rows — its CoWorkBench 73.9 against DeepSeek's 45.1 is the single largest gap in the table — and does it with only 6B active parameters.

DeepSeek V4 Flash leads on nothing in this table except the security/repo niches, but it is competitive almost everywhere and, as the next sections show, wins decisively on speed and cost.

| Benchmark | DeepSeek V4 Flash 0731 | Qwen3.8-Flash-Next | GLM-5.3-Flash |
|---|---|---|---|
| **AA Intelligence Index** (independent) | 50 | 56 | **57** |
| **Terminal-Bench 2.1** (terminal agent) | 82.7 vendor / **79 \[AA\]** | n.r. | **84.3** |
| **DeepSWE 1.1** (agentic coding) | 54.4 | 58.7 | **63.4** |
| **SWE-bench Pro** (multilingual SWE) | 56.0 | **62.5** | n.r. |
| **SWE-bench Multilingual** | n.r. | **81.0** | n.r. |
| **SWE-bench Verified** | **79.0** (April card, Max) | n.r. | n.r. |
| **LiveCodeBench v6** | 90.6 | **91.9** | n.r. |
| **GPQA Diamond** (reasoning) | 90.8 | **91.7** | n.r. |
| **IFBench** (instruction following) | 79.2 | **81.3** | n.r. |
| **CoWorkBench** (long-horizon office) | 45.1 | **73.9** | n.r. |
| **JobBench** (professional tasks) | 41.3 | **55.7** | n.r. |
| **Toolathlon Verified** (tool use) | 70.3 | **73.5** | n.r. |
| **Cybergym** (security agent) | **76.7** | n.r. | n.r. |
| **NL2Repo** (repo generation) | **54.2** | n.r. | n.r. |
| **AutomationBench** | 25.1 (public) | n.r. | **48.8** |
| **OfficeQA Pro** | n.r. | n.r. | **62.4** |
| **Agents' Last Exam** (pass@1) | **25.2** | 24.3 (score 51.2) | n.r. |
| **HLE** (frontier reasoning) | 33.8 | 35.9 | 55.3\* |
| **AndroidWorld** (mobile use) | — | **84.5** | n.r. |
| **MathVision** (visual math) | — | **95.7** (with code interp.) | n.r. |
| **Z.ai Code Bench v1.0 (max)** | n.r. | n.r. | 29.0 (Opus 4.8: 29.5) |

*n.r. = not reported. \*GLM's HLE figure comes from a different, tool-augmented setup and is not comparable to the text-only runs of the other two.*

![](https://regolo.ai/wp-content/uploads/2026/09/image-9-1024x521.png)## Independent verification: Artificial Analysis

Vendor tables are marketing documents, so the independent column matters most. Artificial Analysis has measured all three models on its standardized harness:

| Metric (AA, first-party API) | DeepSeek V4 Flash 0731 (max) | Qwen3.8-Flash-Next | GLM-5.3-Flash |
|---|---|---|---|
| Intelligence Index | 50 | 56 | **57** |
| Output speed | **107.9 tok/s** | 87.8 tok/s | 48.7 tok/s |
| Time to first token | **1.50s** | ~competitive | 1.52s |
| Tokens to complete the Index | ~206M (down from 234M in April) | n.r. | ~150M (class median: 110M) |

Reference points: the open-weight class median output speed is ~67 tok/s, and GLM-5.3-Flash's 57 on the Intelligence Index lands just 3 points behind the full GLM-5.3 (60) at roughly one-tenth the token price. DeepSeek's 0731 checkpoint gained +10 points on the Index over the April preview purely from re-done post-training — same 284B/13B architecture. DeepSeek also holds the #2 open-weight spot on AA's GDPval v2 Elo (1559), behind Kimi K3 (1687).

Note the verbosity column: GLM-5.3-Flash burns ~150M tokens to finish the Index (36% above the class median) and generates them at half the median speed. That is the "cheap tokens, expensive minutes" effect — decisive for interactive use, irrelevant for overnight batch jobs.

![](https://regolo.ai/wp-content/uploads/2026/09/benchmark-artificial-analysis-1024x699.png)## Pricing Snapshot (per 1M tokens, September 2026)

| Model | Input | Cached input | Output | Blended (3:1 in:out) | Notes |
|---|---|---|---|---|---|
| **DeepSeek V4 Flash** | $0.14 | **$0.0028** (~98% off) | $0.28 | ~$0.18 | OpenRouter providers from ~$0.03/$0.16; 2,500 concurrency limit (5× V4 Pro) |
| **Qwen3.8-Flash-Next** | $0.15 | varies by provider | $0.47 | ~$0.23 | Blended ~$0.23 measured; thinking tokens bill as output |
| **GLM-5.3-Flash** | $0.15 | $0.03 | $0.50 | ~$0.24 | **50% launch promo until Sep 9**: $0.075/$0.25, cached $0.015 → blended ~$0.12 |

Cost per completed task tells the real story: on AA's Intelligence Index, GLM-5.3-Flash runs about **$0.09 per task** at list price (~$0.045 on the promo tier) versus $0.68 for GLM-5.3 — and DeepSeek's cost per task measured ~60% below GPT-5.6 Luna (max). One billing trap applies to all three: reasoning tokens are charged at output rates, so agentic workloads cost more than the headline input price suggests.

For detailed token math and tier comparisons, consult our [model pricing benchmarks](https://regolo.ai/compare/) directory and see the [GLM token pricing breakdown and Core plan savings](https://regolo.ai/compare/glm-5.2-pricing-breakdown/) to calculate operational break-even points under European Zero Data Retention.

## Speed and Latency

The word "Flash" is doing different work in each name:

- **DeepSeek V4 Flash** — 107.9 tok/s on the first-party API, ~123 tok/s P50 across OpenRouter providers, 1.50s TTFT. The fastest of the tier by a wide margin, helped by DSpark speculative decoding.
- **Qwen3.8-Flash-Next** — 87.8 tok/s, comfortably above the ~67 tok/s class median. The best speed-per-intelligence point in the group.
- **GLM-5.3-Flash** — 48.7 tok/s despite a snappy 1.52s TTFT. Quick to start, slow to finish, and verbose on top. Fine for async pipelines; painful in interactive coding loops.

![](https://regolo.ai/wp-content/uploads/2026/09/output-throughput-1024x478.png)## Why These Models Are So Efficient (Architecture Notes)

- **Qwen3.8-Flash-Next** is a public preview of the Qwen4 architecture: a 125B MoE paired with a 51B **n-gram embedding layer** (an idea borrowed from DeepSeek's Engram paper) that acts as a cheap lookup memory, letting the model activate only ~6B parameters per token while matching 49B-active models on coding.
- **GLM-5.3-Flash** is the first GLM-5 model with **hybrid attention** (KDA linear layers interleaved with NoPE sparse MLA) plus **IndexPool** key compression: ~3× less attention compute and a 4.4× smaller KV cache than GLM-5.3 at million-token context. Trained on a 30T-token multimodal corpus; native FP8 weights (~306 GiB — self-hosting realistically requires an 8× Hopper-class node).
- **DeepSeek V4 Flash 0731** changed no architecture at all — the entire +10 Intelligence Index jump came from re-running post-training (DeepSWE went 7.3 → 54.4 on identical weights). A reminder that in 2026, post-training is a release cycle.

## Operational guidance by Use Case

**Coding & code review** — GLM leads agentic coding (DeepSWE 63.4, Terminal-Bench 84.3, within half a point of Opus 4.8 on Z.ai's internal code bench). Qwen leads repository-level fixes (SWE-bench Pro 62.5, SWE-bench Multilingual 81.0). DeepSeek remains excellent value (SWE-bench Verified 79, Terminal-Bench 79 \[AA\]) at the lowest cost per token. *Winner, agentic sessions:* GLM. *Winner, repo-scale fixes:* Qwen. *Winner, volume:* DeepSeek.

**Long-document analysis** — All three reach ~1M tokens. GLM's IndexPool was designed exactly for this; DeepSeek is proven at the extreme end; Qwen needs YaRN beyond 262K. *Winner:* GLM / DeepSeek tie.

**Batch extraction & structured output** — DeepSeek's 108 tok/s + $0.0028 cached input is the default for high-volume pipelines (for GDPR compliance without China routing, deploy via the [DeepSeek European sovereign API](https://regolo.ai/alternatives/deepseek-european-api/)). Qwen (IFBench 81.3) and GLM are more accurate on complex schemas. *Winner, volume:* DeepSeek. *Winner, accuracy:* Qwen.

**Multimodal / OCR / UI work** — Only Qwen and GLM qualify natively. Qwen posts AndroidWorld 84.5 and MathVision 95.7; GLM takes image + video and powers Browser/Computer Use in ZCode, but trails Gemini 3.7 Flash on video benchmarks. *Winner:* Qwen for static visual tasks, GLM for video and agentic UI.

**Interactive products (chat, pair-programming)** — Throughput is the product here. DeepSeek or Qwen; GLM's 49 tok/s will be felt by users. *Winner:* DeepSeek.

**Overnight / async agentic jobs** — GLM's promo pricing (blended ~$0.12 until Sep 9) plus top agentic scores make it close to ideal. *Winner:* GLM.

## Composite scores

Normalized 0–100, combining the benchmark table above, AA independent data, pricing, context, output capacity, recency, and versatility:

| Metric | DeepSeek V4 Flash | Qwen3.8-Flash-Next | GLM-5.3-Flash |
|---|---|---|---|
| Capabilities | 78 | 85 | **89** |
| Pricing efficiency | **94** | 82 | 88 |
| Context window | **95** | 88 | **95** |
| Output capacity | **92** | 80 | 85 |
| Recency | 75 | **95** | **95** |
| **Composite** | 84 | 86 | **90** |

- **GLM-5.3-Flash** (90) — strongest composite claim
- **Qwen3.8-Flash-Next** (86) — best efficiency story, most complete language/agentic table
- **DeepSeek V4 Flash** (84) — volume and speed king

![](https://regolo.ai/wp-content/uploads/2026/09/benchmark_pentagon-1024x907.png)## A Necessary Caveat on Vendor Numbers

Every non-AA figure above is self-reported, and reproducibility gaps are already documented: AA's independent Terminal-Bench 2.1 run scored DeepSeek 79% versus the vendor's 82.7, and an independent DeepSWE re-test of DeepSeek's V4 Pro measured pass@1 far below the official table. Qwen reports the *highest* score across two harnesses (Claude Code and mini-SWE-agent) for DeepSWE. GLM's HLE 55.3 uses a tool-augmented setup. None of this means the numbers are fake — it means you should replicate the two or three benchmarks that map to your workload before committing.

---

  SOVEREIGN EUROPEAN INFERENCE 

###  Run DeepSeek, Qwen, and GLM in Europe with Zero Data Retention 

 Get 600 Million tokens on the Regolo Core plan (€39/mo flat, ~€0.065/1M). Switch endpoints in 1 line of code with full OpenAI SDK compatibility on 100% green datacenters.

 [ Start 30-day free trial (no card required) → ](https://regolo.ai/pricing/?utm_source=blog&utm_medium=bento_cta&utm_campaign=deepseek-flash-bottom)  Free credits included · 100% green energy  

  ✓ 100% EU Green Datacenters   ✓ Certified ZDR  

 

 

---

## Practical recommendation

- **Best overall quality-to-price, especially async/agentic** → **GLM-5.3-Flash**. Top independent intelligence (57), top agentic coding (DeepSWE 63.4), MIT weights, and promo pricing until Sep 9. Accept the 49 tok/s.
- **Best speed + cost at volume** → **DeepSeek V4 Flash**. 108+ tok/s, $0.14/$0.28 with near-free cache hits, 2,500 concurrency. The high-throughput default.
- **Best balance and best architecture bet** → **Qwen3.8-Flash-Next**. 6B active parameters delivering 56 on the AA Index, the strongest language/office-agent table, native multimodal, 88 tok/s.

The right engineering answer is routing, not loyalty: send simple high-volume traffic to the cheapest capable endpoint (usually DeepSeek), escalate agentic and multimodal work to GLM or Qwen, and re-run your own eval set every few weeks — this tier now moves on a monthly cadence.

---

## FAQ

**Which Flash model has the best quality-to-price in September 2026?**
GLM-5.3-Flash for async and agentic workloads (AA Index 57 at ~$0.09/task list, ~$0.045 on promo). DeepSeek V4 Flash for sustained high-volume text. Qwen3.8-Flash-Next for the best all-round balance.

**Is GLM-5.3-Flash actually fast?**
No. Despite the name it generates ~49 output tokens/s — below the 67 tok/s class median and about half the flagship GLM-5.3's speed — though its 1.52s time-to-first-token is competitive. Use it for batch, not interactive loops.

**How long does the GLM-5.3-Flash promo last?**
The 50% launch discount ($0.075 input / $0.25 output per 1M tokens) runs until September 9, 2026 on Z.ai's API; some third-party gateways run parallel promos.

**Can I self-host these models?**
All three ship open weights. GLM-5.3-Flash needs ~306 GiB (FP8) and Hopper-or-newer GPUs for the current vLLM path. DeepSeek's official recipe targets a 4× GB300 node. Qwen3.8-Flash-Next has an official FP8 variant, and community quantizations run on a single desktop (≈11 tok/s at 2-bit CPU, up to ~52 tok/s with GPU offload) — usable for evals, not production.

**Which model is best for agentic coding?**
On published numbers: GLM-5.3-Flash (DeepSWE 63.4, Terminal-Bench 84.3) for long agentic sessions; Qwen3.8-Flash-Next (SWE-bench Pro 62.5) for repository-level fixes; DeepSeek V4 Flash for cost-effective volume (SWE-bench Verified 79).

**Should I trust vendor benchmark tables?**
Directionally, yes; literally, no. Independent re-runs show gaps of ~4 points (Terminal-Bench) and occasionally more. Reproduce the benchmarks that match your workload on your own harness before locking a routing decision.

---

## Ship Private AI. Not Infrastructure.

You have the private AI App architecture, bow give it an inference layer built for production.

**Regolo** gives European teams fast, OpenAI-compatible access to Mistral, Llama, Qwen, DeepSeek, GLM, and more — with zero data retention, EU data residency, and no new SDK to learn.

Change your `base_url`. Keep your LangChain code. Start shipping.

### 🚀 [Start your 30-day free trial →](https://regolo.ai/?utm_source=blog&utm_medium=cta&utm_campaign=private-rag)

Build, test, and deploy with no infrastructure to maintain.
**No credit card. No migration project. No compromise on data control.**

### 💬 [Join the Regolo Discord →](https://discord.gg/bqGrVJHeF)

Meet builders working on private RAG, local LLMs, LangChain, Ollama, and production AI systems. Share your setup, get feedback from the community, and speak directly with the Regolo team.

### 🤝 [Talk to an AI Infrastructure Engineer →](https://regolo.ai/contact?utm_source=blog&utm_medium=cta&utm_campaign=private-rag)

Running a sensitive workload, scaling beyond a proof of concept, or assessing a managed EU inference provider? Get a tailored architecture and commercial proposal for your team.

### 📂 [Clone the GitHub repository →](https://github.com/regolo-ai/tutorials/)

Get the full implementation from this guide: ingestion scripts, ChromaDB setup, hybrid retrieval, the **30-Question RAG Floor**, evaluation examples, and deployment configuration.

> **Private AI should not require a private data center.**
> Regolo gives your team an EU-native path from local experimentation to production-grade inference.

---

### Build with Regolo

- **Discord:** [Join the community →](https://discord.gg/bqGrVJHeF)
- **GitHub:** [Explore open-source workflows →](https://github.com/regolo-ai/tutorials/)
- **X / Twitter:** [Follow @regolo\_ai →](https://x.com/regolo_ai)
- **Reddit:** [Join the community →](https://www.reddit.com/r/regolo_ai/)
- **Documentation:** [Read the API docs →](https://docs.regolo.ai)
- **Contact:** [Talk to the team →](https://regolo.ai/contact)

---

*Built with ❤️ by the Regolo team. Questions? [regolo.ai/contact](https://regolo.ai/contact)* or chat with us on [Discord](https://discord.gg/bqGrVJHeF)