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Model A
Mistral Large 3

Mistral

48.77/100

Supported · Public rank #149

90% interval 27.470.1

Mistral Large 3 vs Qwen3.6-27B

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Alibaba logo
Model B
Qwen3.6-27B

Alibaba

52.73/100

Estimated · Public rank #120

90% interval 47.058.5

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Qwen3.6-27B

    Qwen3.6-27B leads on the public coding lane, 42.6 to 26, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Qwen3.6-27B

    Qwen3.6-27B has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls
  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Mistral Large 3 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
Mistral Large 3 only
0
Qwen3.6-27B only
38
Like-for-like categories
1 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Coding

Like-for-like
Mistral Large 3
26.0
Supported · #176/183
Qwen3.6-27B
42.6
Supported · #128/183
Basis
BenchAlign lane · 0 vs 6 public rows
Reading
Qwen3.6-27B leads · intervals overlap

Agentic

Directional only
Mistral Large 3
42.8
Estimated · #111/151
Qwen3.6-27B
33.9
Supported · #133/151
Basis
BenchAlign lane · 0 vs 6 public rows
Reading
Directional only

Knowledge

Directional only
Mistral Large 3
43.8
Estimated · #124/181
Qwen3.6-27B
49.0
Estimated · #95/181
Basis
BenchAlign lane · 0 vs 6 public rows
Reading
Directional only

Instruction following

Directional only
Mistral Large 3
41.4
#100/120
Qwen3.6-27B
82.2
#50/120
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
Mistral Large 3
46.0
Unranked · 2 rankable rows
Qwen3.6-27B
73.7
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Mistral Large 3
Not ranked
Qwen3.6-27B
72.8
Unranked · 5 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Mistral Large 3
Not ranked
Qwen3.6-27B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Mistral Large 3
41.8
Unranked · 1 rankable row
Qwen3.6-27B
51.5
#35/48
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Mistral Large 3
$0.00125
Fits in one request
Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request

Qwen3.6-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Mistral Large 3
$0.0295
Fits in one request
Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request

Qwen3.6-27B has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Mistral Large 3
$0.125
Fits in one request
Cached input priced at the published list-input rate
Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6-27B has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

Mistral Large 3

256K

Qwen3.6-27B

262K

API model ID

Mistral Large 3

Not sourced

Qwen3.6-27B

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Mistral Large 3

Not published

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

Mistral Large 3

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

Mistral Large 3

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

Mistral Large 3

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

Mistral Large 3

Non-Reasoning

Qwen3.6-27B

Reasoning

Weight access

Mistral Large 3

Proprietary

Qwen3.6-27B

Open Weight

License

Mistral Large 3

Proprietary

Qwen3.6-27B

Open Weight

Release date

Mistral Large 3

2025-12-02

Qwen3.6-27B

2026-04-21

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Qwen3.6-27B has the larger documented window (262K).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Mistral Large 3
API / mo$1,500
Self-host / mo$9,110
Break-even380M/day
Qwen3.6-27B
API / mo$0
Self-host / mo$429
Break-even
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence38 rows

Agentic

  • Terminal-Bench 2.0

    Mistral Large 3
    Qwen3.6-27B59.3%
    Source

    Not directly comparable

  • Claw-Eval

    Mistral Large 3
    Qwen3.6-27B72.4%
    Source

    Not directly comparable

  • QwenClawBench

    Mistral Large 3
    Qwen3.6-27B53.4%
    Source

    Not directly comparable

  • QwenWebBench

    Mistral Large 3
    Qwen3.6-27B1487
    Source

    Not directly comparable

  • AndroidWorld

    Mistral Large 3
    Qwen3.6-27B70.3%
    Source

    Not directly comparable

  • Gert Labs

    Mistral Large 3
    Qwen3.6-27B54.84%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Mistral Large 3
    Qwen3.6-27B77.2%
    Source

    Not directly comparable

  • SWE Multilingual

    Mistral Large 3
    Qwen3.6-27B71.3%
    Source

    Not directly comparable

  • SWE-bench Pro

    Mistral Large 3
    Qwen3.6-27B53.5%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Mistral Large 3
    Qwen3.6-27B59.3%
    Source

    Not directly comparable

  • LiveCodeBench

    Mistral Large 3
    Qwen3.6-27B83.9%
    Source

    Not directly comparable

  • NL2Repo

    Mistral Large 3
    Qwen3.6-27B36.2%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    Mistral Large 3
    Qwen3.6-27B86.2%
    Source

    Not directly comparable

  • MMLU-Redux

    Mistral Large 3
    Qwen3.6-27B93.5%
    Source

    Not directly comparable

  • SuperGPQA

    Mistral Large 3
    Qwen3.6-27B66%
    Source

    Not directly comparable

  • C-Eval

    Mistral Large 3
    Qwen3.6-27B91.4%
    Source

    Not directly comparable

  • GPQA

    Mistral Large 3
    Qwen3.6-27B87.8%
    Source

    Not directly comparable

  • HLE

    Mistral Large 3
    Qwen3.6-27B24%
    Source

    Not directly comparable

Math

  • HMMT Feb 2025

    Mistral Large 3
    Qwen3.6-27B93.8%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Mistral Large 3
    Qwen3.6-27B90.7%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Mistral Large 3
    Qwen3.6-27B84.3%
    Source

    Not directly comparable

  • MMAnswerBench

    Mistral Large 3
    Qwen3.6-27B80.8%
    Source

    Not directly comparable

  • AIME26

    Mistral Large 3
    Qwen3.6-27B94.1%
    Source

    Not directly comparable

Multimodal

  • MMMU

    Mistral Large 3
    Qwen3.6-27B82.9%
    Source

    Not directly comparable

  • MMMU-Pro

    Mistral Large 3
    Qwen3.6-27B75.8%
    Source

    Not directly comparable

  • RealWorldQA

    Mistral Large 3
    Qwen3.6-27B84.1%
    Source

    Not directly comparable

  • DynaMath

    Mistral Large 3
    Qwen3.6-27B85.6%
    Source

    Not directly comparable

  • MStar

    Mistral Large 3
    Qwen3.6-27B81.4%
    Source

    Not directly comparable

  • SimpleVQA

    Mistral Large 3
    Qwen3.6-27B56.1%
    Source

    Not directly comparable

  • CharXiv

    Mistral Large 3
    Qwen3.6-27B78.4%
    Source

    Not directly comparable

  • CC-OCR

    Mistral Large 3
    Qwen3.6-27B81.2%
    Source

    Not directly comparable

  • CountBench

    Mistral Large 3
    Qwen3.6-27B97.8%
    Source

    Not directly comparable

  • RefCOCO (avg)

    Mistral Large 3
    Qwen3.6-27B92.5%
    Source

    Not directly comparable

  • ERQA

    Mistral Large 3
    Qwen3.6-27B62.5%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    Mistral Large 3
    Qwen3.6-27B87.7%
    Source

    Not directly comparable

  • VideoMMMU

    Mistral Large 3
    Qwen3.6-27B84.4%
    Source

    Not directly comparable

  • MLVU (M-Avg)

    Mistral Large 3
    Qwen3.6-27B86.6%
    Source

    Not directly comparable

  • V*

    Mistral Large 3
    Qwen3.6-27B94.7%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Mistral Large 3 or Qwen3.6-27B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Mistral Large 3 or Qwen3.6-27B?

Qwen3.6-27B leads the public coding lane, 42.6 to 26, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Mistral Large 3 or Qwen3.6-27B?

Mistral Large 3 scores higher for agentic tasks on the public lane, 42.8 to 33.9. Mistral Large 3 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Mistral Large 3 or Qwen3.6-27B?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Mistral Large 3 or Qwen3.6-27B?

Qwen3.6-27B has the larger documented context window: 262K, compared with 256K.

Related comparisons

Last updated September 4, 2026

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