Why a model scoring 43% on DeepSWE beats one scoring 60%: reading the DeepSeek 1017, Muse Spark 816B, and Beam 501B benchmarks

Between October 15 and 22, 2026, three major open-weight AI models were released on Hugging Face within days of each other: Meta Muse Spark 816B, DeepSeek V4.1 Flash 1017, and Reflection Beam 501B. Every launch chart claims its model is at the top of the leaderboard.

If you put their official charts side by side, the numbers seem to make no sense. On one famous coding test (DeepSWE v1.1), Reflection’s Beam scores 43.3%, Meta’s Muse Spark scores 60.0%, and DeepSeek 1017 hits 77.0%. Yet on another industry-standard coding test (SWE-bench Verified), Beam scores 81.6%, and independent tests on Artificial Analysis show that Beam is the fastest model in the group (327 tokens per second) and the cheapest to run ($0.23 per task).

How can the same model be both a 43% coder and an 81.6% coder? Because benchmarks do not test “intelligence” in the abstract, they test specific ways of working. In this guide, we combine the vendor charts with independent tests from Artificial Analysis (plus GLM-5.3 as a reference point) to explain, in plain English and with everyday examples, how to read AI benchmarks and pick the right model for your team.

Quick summary (TL;DR): pick DeepSeek 1017 if you use autonomous coding tools like Claude Code or OpenCode that edit many files and run terminal commands ($0.26/task, tied #1 in overall intelligence). Pick Beam 501B if you need super-fast single-file code fixes, pull-request reviews, or low cloud bills ($0.23/task, 327 tok/s, 2.7× less thinking chatter). Pick Muse Spark 816B if you are building a customer-facing assistant that needs to read images, audio, or video, connect to dozens of company apps, and block 99.2% of user jailbreak attempts ($0.84/task).

BENCHMARK · ARTIFICIAL ANALYSIS V4.3.2 & HF MODEL CARDS (OCT 2026)

DeepSeek 1017 leads coding and intelligence; Beam 1.0 wins speed and token cost.

Comparing published model cards against independent Artificial Analysis v4.3.2 evaluations across 10 benchmarks (October 15 to 22, 2026).

Permissive open weights (MIT / Apache 2.0) Custom license baseline (Muse / GLM-5.3)

Why 43% beats 60%: SWE-bench vs DeepSWE v1.1

Beam (SWE-V)81.6%

DeepSeek 101777.0%

Muse Spark60.0%

Beam (DeepSWE)43.3%

AA Intelligence Index v4.3.2 (10 evaluations)

DeepSeek 101745 (#2)

GLM-5.3 (Max)45 (#3)

Muse Spark39 (#1 US)

Beam 1.030 (#2 US)

Weighted cost per AA Index task (lower is better)

Beam 1.0$0.23

DeepSeek 1017$0.26

Muse Spark$0.84

GLM-5.3 (Max)$2.01

Output throughput on Artificial Analysis (tok/s)

Beam (23B act)326.9 t/s

DeepSeek (16B)203.7 t/s

Muse (42B act)156.3 t/s

GLM-5.3 (40B)76.7 t/s

2.66×fewer output tokens on AA Index for Beam 1.0
(79M tokens vs 210M for DeepSeek 1017 and GLM-5.3)

326.9 t/smedian output speed for Beam 1.0 (23B active)
(4.5× faster than the 72.4 t/s open-weight class median)

$0.23 vs $2.01cost per AA Index task across frontier open weights
(Beam $0.23, DeepSeek $0.26, Muse $0.84, GLM $2.01)

Sources: Artificial Analysis Intelligence Index v4.3.2 (10 evaluations, >150B open-weight class median: 19 Index score, 140M tokens, 72.4 tok/s) and official Hugging Face model cards as of October 22, 2026.

The 4 models compared in plain numbers

You do not need to memorize how many internal layers or parameters a model has. Only two hardware facts matter for your business: first, all four models are so large (requiring 300 GB to 2,000 GB of graphics memory) that renting a dedicated GPU cluster just for your company rarely makes financial sense, so calling them via an API is the practical choice.

Second, each model activates only a small slice of its brain per word, which is why Beam and DeepSeek type much faster than GLM-5.3.

BENCHMARK · FRONTIER MOE ON REGOLO

Output speed (tokens/second)

Active parameters drive speed: Beam (23B) hits 327 tok/s, DeepSeek (16B) 204 tok/s.

Beam 1.0326.9 tok/s

DeepSeek203.7 tok/s

Muse Spark156.3 tok/s

GLM-5.376.7 tok/s

Artificial Analysis throughput (TTFT: 0.78s Muse, 0.80s Beam, 1.03s DeepSeek, 2.90s GLM). Class median: 72.4 tok/s. Sources: Artificial Analysis Intelligence Index v4.3.2 and official HF model cards (measured Oct 15 to 22, 2026).

Rule 1: a quick code fix is not the same job as a 20-step terminal session

Imagine two different tasks you give a software developer:

• Job A (The quick patch): You paste a 200-line Python file and an error message into chat and ask, “Where is the bug, and what is the fix?” The AI reads the file once and gives you the corrected code. That is what SWE-bench Verified tests. On this job, Beam 501B is extraordinary, scoring 81.6%.
• Job B (The autonomous terminal agent): You open an agent tool like Claude Code or OpenCode and say, “Update our login system across the whole project and make sure all tests pass.” Here, the AI has to search folders, edit 10 files, run the test command in the terminal, read the failure log when something breaks, and try again for 15 turns in a row. That is what DeepSWE v1.1 and Terminal-Bench 4.0 test. On this job, DeepSeek 1017 wins with 77.0%, while Beam drops to 43.3%.

BENCHMARK · FRONTIER MOE ON REGOLO

Why 43% on DeepSWE beats 60%

Beam trails on agentic DeepSWE v1.1 (43.3%), but tops SWE-bench Verified (81.6%).

Beam (SWE)81.6%

DeepSeek77.0%

Muse Spark60.0%

Beam (DSWE)43.3%

Published model cards (Oct 2026). Beam (SWE) is SWE-bench Verified; the other three bars are DeepSWE v1.1 (GLM-5.3 scores 66.9%). Sources: Artificial Analysis Intelligence Index v4.3.2 and official HF model cards (measured Oct 15 to 22, 2026).

There is also a “version trap” to watch out for on marketing slides. Almost every model today scores above 75% on the old Terminal-Bench 2.1 test because models have practiced those exact questions. When Artificial Analysis tested the models on the brand-new, much harder Terminal-Bench 4.0, scores dropped across the board. Always check which version of a benchmark a company is quoting:

Rule 2: a “talkative” thinking model can cost you 8× more per task

Modern AI models “think out loud” in a hidden scratchpad before they show you the final answer. AI providers charge you for every single word in that hidden scratchpad as output tokens.

On the independent Artificial Analysis Intelligence Index (which combines 10 different tests across coding, math, science, and office work), DeepSeek 1017 and GLM-5.3 tie at the top with 45 points, followed by Muse Spark at 39 and Beam at 30:

BENCHMARK · FRONTIER MOE ON REGOLO

AA Intelligence Index v4.3.2

DeepSeek 1017 and GLM-5.3 tie at 45; Muse Spark leads US open models at 39.

DeepSeek45 (#2 open)

GLM-5.345 (#3 open)

Muse Spark39 (#1 US)

Beam 1.030 (#2 US)

Independent Artificial Analysis v4.3.2 evaluation across 10 benchmarks. Open-weight >150B class median is 19. Sources: Artificial Analysis Intelligence Index v4.3.2 and official HF model cards (measured Oct 15 to 22, 2026).

Now look at what happens to your wallet when you run those exact tests:

BENCHMARK · FRONTIER MOE ON REGOLO

Cost per AA Index task (USD)

Beam outputs 79M tokens (vs 210M for DeepSeek and GLM) to hit $0.23 per task.

Beam 1.0$0.23 (79M)

DeepSeek$0.26 (210M)

Muse Spark$0.84 (110M)

GLM-5.3$2.01 (210M)

Artificial Analysis weighted average cost per task and total output tokens on the Intelligence Index. Lower is better. Sources: Artificial Analysis Intelligence Index v4.3.2 and official HF model cards (measured Oct 15 to 22, 2026).

A concrete example of why your bill changes so much: Suppose you run 1,000 automated tasks every day. To finish the Artificial Analysis test suite, Beam wrote 79 million tokens because it keeps its internal thinking short and direct. DeepSeek 1017 and GLM-5.3 both wrote 210 million tokens (nearly 3 times as much internal monologue for the same test).

Because GLM-5.3 is both talkative (210M tokens) and charges a high price per token ($4.40 per million output tokens), an average task costs $2.01. DeepSeek 1017 is just as talkative, but its per-token price is much lower ($1.20 per million output tokens, plus a 98% discount when it re-reads cached files), bringing its real cost down to $0.26 per task. Beam combines low prices with short thinking to hit $0.23 per task.

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Rule 3: connecting to company apps vs answering from memory

When you test an AI model without internet access or external tools, even the biggest models get more than half of expert-level questions wrong. The moment you allow the model to use tools (such as searching documentation or running a Python script to check a calculation), accuracy jumps from around 30% to over 60%.

In day-to-day company work, this comes down to three practical questions:

• Do you connect the AI to lots of business apps (Slack, Jira, CRM, databases)? Many teams now use MCP (Model Context Protocol) to plug 10 or 20 tools into one assistant. On MCP-Atlas, the benchmark that tests whether a model picks the right tool and fills in the right fields without getting confused, Meta Muse Spark leads at 89.2% (ahead of GLM-5.3 at 84.2% and Beam at 81%).
• Does your assistant need to read screenshots, audio, or video? Beam and GLM-5.3 only read text. DeepSeek 1017 reads text and images (great for reading charts or website screenshots). Muse Spark is the only model of the four that natively understands text, images, audio, and video in a single prompt.
• What happens when the model does not know an answer? Artificial Analysis’s factual reliability test revealed an important quirk about Beam 1.0: it knows more raw facts than smaller models (33% correct from memory), but when it does not know, it guesses wrong 47% of the time instead of saying “I don’t know.” If you use Beam to answer questions over your company PDFs, always add a clear rule in your system prompt: “Answer only using the provided documents. If the fact is missing, reply that you do not know.”

Rule 4: public chatbot safety and commercial licenses

Before you put an open-weight model into production, check whether it will embarrass your brand in front of customers and whether its license fits your business model:

• If external customers talk to your bot, pick Muse Spark: When researchers tried to trick these models into bypassing safety rules without extra filters, Muse Spark blocked 99.2% of attacks (letting only 0.5% through on the StrongREJECT v2 safety test). By contrast, GLM-5.3 and most other open models let over 60% of adversarial prompts through unless you add your own guardrail layer.
• If you use AI internally to find security bugs in your own code, pick GLM-5.3 or Beam: Because GLM-5.3 is less restrictive on security topics, it is the best model for defensive security teams, scoring 84.5% on CyberGym (finding and fixing software vulnerabilities). For tighter budgets, Beam 1.0 ranks #1 among US open models on the Artificial Analysis Cyber Index (34%) at one-eighth the cost per run.
• Check the license in 10 seconds: DeepSeek 1017 (MIT) and Beam 1.0 (Apache 2.0) have no strings attached; you can use them commercially at any scale. Meta Muse Spark is free for most companies, but requires displaying “Built with Muse Spark Open” and signing a commercial agreement if your AI product generates over $20 million in annual revenue.

Visual guide: which model should your team pick?

Use the diagram below to match your daily work and target customer profile (ICP) to the right model:

DECISION MAP · ACTIVITY & BENCHMARK → MODEL → TARGET ICP

Which model fits your team? Follow your daily workflow

Match what your team actually builds (left) to the benchmark winner (center) and the ideal customer profile (right).

1. Your daily activity & benchmark proof

2. Model to pick

3. Ideal customer profile (ICP)

Activity · Autonomous multi-file coding

Agent edits 10+ files, runs tests in the terminal, reads errors, and retries until green

Proof: 77.0% on DeepSWE v1.1 · 42.3% Terminal-Bench 4.0 · 45 AA Index

→

Best all-round coder

DeepSeek 1017

$0.26 / task 204 tok/s MIT

→

Target ICP · Product Engineering Teams

Startups & dev teams using Claude Code, OpenCode, or Cursor

You want top coding intelligence without paying $2+ per task, and 98% cache discounts when the agent re-reads your repo.

Activity · Fast single-shot patches & CI reviews

Review every pull request, write unit tests, or fix one file while a developer waits

Proof: 81.6% SWE-bench Verified · 327 tok/s · 2.7× less thinking chatter

→

Fastest & cheapest

Reflection Beam 1.0

$0.23 / task 327 tok/s Apache 2.0

→

Target ICP · High-Volume SaaS & DevEx Teams

Platform leads running thousands of automated PR checks a day

You need instant replies in the IDE and predictable cloud bills, without paying for 200M tokens of rambling chain-of-thought.

Activity · Customer-facing multimodal & tool routing

Public chatbot that reads screenshots, video, or audio and calls 20+ company apps (MCP)

Proof: 89.2% MCP-Atlas · Blocks 99.2% of jailbreak attacks

→

Safest & multimodal

Muse Spark 816B

$0.84 / task Audio/Video Muse License

→

Target ICP · Enterprise Customer Ops & AI Products

Companies exposing AI directly to external users across CRM, Slack, and DBs

You cannot risk a customer tricking the bot on social media, and your users upload screenshots, PDFs, or voice notes.

Activity · Nightly security & vulnerability hunting

Overnight batch jobs that audit commits for security flaws and test patches in a sandbox

Proof: 84.5% on CyberGym (GLM-5.3) · 34% AA Cyber Index (#1 US for Beam)

→

Security specialist

GLM-5.3 / Beam 1.0

84.5% CyberGym Batch / Async

→

Target ICP · AppSec, FinTech & Regulated CISOs

Security & compliance teams running unattended overnight audits

Nobody is waiting in real time, so GLM-5.3’s slower 77 tok/s speed is fine, or use Beam 1.0 to run 8× more scans for the same budget.

Tip: Most production teams on Regolo combine two models, DeepSeek 1017 for deep multi-file tasks and Beam 1.0 (or a fast Flash model) for high-volume single-shot checks.

As we saw in our quality-to-price comparison of the Flash tier, you do not have to marry a single model. The smartest setup is routing: use DeepSeek 1017 when an agent needs to work through a complex multi-file task, and use Beam 1.0 (or a fast Flash model) for everyday single-file tasks where speed and low token bills matter most.

Frequently asked questions

Why does Beam score 43.3% on DeepSWE v1.1 but 81.6% on SWE-bench Verified?
Because they test two different workflows. SWE-bench Verified tests whether a model can fix a bug in a single pass when given the right context, where Beam excels (81.6%). DeepSWE v1.1 tests whether an agent can spend 15 to 20 turns exploring a terminal, running tests, and recovering from errors, where DeepSeek 1017 leads (77.0%).

How do these models rank on the independent Artificial Analysis Intelligence Index?
On Artificial Analysis v4.3.2 (which averages 10 benchmarks), DeepSeek V4.1 Flash 1017 and GLM-5.3 tie at 45 points (#2 and #3 among all 121 open-weight models), Meta Muse Spark Open scores 39 (#1 US open-weight model), and Reflection Beam 1.0 scores 30 (#2 US open-weight model), all far above the 19-point average for large open models.

Why is GLM-5.3 ($2.01/task) so much more expensive per task than Beam ($0.23) and DeepSeek 1017 ($0.26)?
GLM-5.3 and DeepSeek 1017 both write 210 million tokens of internal thinking across the test suite, compared to just 79 million for Beam. DeepSeek stays cheap ($0.26/task) because its per-token price is low ($0.30/$1.20 per 1M) with a 98% cache discount, while GLM-5.3 charges $1.40/$4.40 per 1M tokens.

Which model is the fastest for real-time developer tools?
Reflection Beam 1.0 is the fastest at 326.9 tokens per second (starting in 0.80 seconds), followed by DeepSeek 1017 at 203.7 tok/s, Muse Spark Open at 156.3 tok/s, and GLM-5.3 at 76.7 tok/s.

Which model is safest to put in front of external customers?
Meta Muse Spark Open blocks 99.2% of jailbreak attempts out of the box (0.5% failure rate on StrongREJECT v2), whereas most other open-weight models require an external guardrail layer when exposed to untrusted users.