Agentic
Like-for-like- Mistral Medium 3.5 128B
- 21.9
- Supported · #150/152
- Qwen3.6-27B
- 30.9
- Supported · #140/152
- Basis
- BenchAlign lane · 3 vs 6 public rows
- Reading
- Qwen3.6-27B leads · intervals overlap
Mistral Medium 3.5 128B vs Qwen3.6-27B
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Qwen3.6-27B has the higher public score, 47.69 versus 30.13, and the 90% score intervals do not overlap.
2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Agentic work
Tool use, computer use, and multi-step task completion
Qwen3.6-27B
Qwen3.6-27B leads on the public agentic lane, 30.9 to 21.9, 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
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Mistral Medium 3.5 128B is scored on Estimated evidence for coding, 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.
5 Mistral Medium 3.5 128B only
2 Shared
36 Qwen3.6-27B only
- Shared results
- 2
- Mistral Medium 3.5 128B only
- 5
- Qwen3.6-27B only
- 36
- 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
Directional only- Mistral Medium 3.5 128B
- 36.9
- Estimated · #127/151
- Qwen3.6-27B
- 41.7
- Supported · #107/151
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- Directional only
Knowledge
Directional only- Mistral Medium 3.5 128B
- 39.0
- Supported · #140/183
- Qwen3.6-27B
- 46.6
- Estimated · #101/183
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- Directional only
Instruction following
Directional only- Mistral Medium 3.5 128B
- 84.0
- #48/123
- Qwen3.6-27B
- 82.4
- #51/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Mistral Medium 3.5 128B
- 68.6
- Unranked · 2 rankable rows
- Qwen3.6-27B
- 74.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Mistral Medium 3.5 128B
- 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 Medium 3.5 128B
- Not ranked
- Qwen3.6-27B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Mistral Medium 3.5 128B
- 55.6
- Unranked · 1 rankable row
- Qwen3.6-27B
- 51.9
- #35/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Mistral Medium 3.5 128B | Qwen3.6-27B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 21.9Supported · #150/152 | 30.9Supported · #140/152 | Like-for-likeBenchAlign lane · 3 vs 6 public rows | Qwen3.6-27B leads · intervals overlap |
| Coding | 36.9Estimated · #127/151 | 41.7Supported · #107/151 | Directional onlyBenchAlign lane · 2 vs 6 public rows | Directional only |
| Knowledge | 39.0Supported · #140/183 | 46.6Estimated · #101/183 | Directional onlyBenchAlign lane · 2 vs 6 public rows | Directional only |
| Instruction following | 84.0#48/123 | 82.4#51/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 68.6Unranked · 2 rankable rows | 74.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 72.8Unranked · 5 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 55.6Unranked · 1 rankable row | 51.9#35/48 | Not comparableProvisional lane · 0 vs 2 weighted rows | 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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
SWE-bench Verified
Coding
Mistral Medium 3.5 128B: 77.6%Qwen3.6-27B: 77.2%Normalized gap 0.4Mistral Medium 3.5 128B source Qwen3.6-27B source
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 Medium 3.5 128B
- $0.00525
- 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 Medium 3.5 128B
- $0.0975
- 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 Medium 3.5 128B
- $0.405
- 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 Medium 3.5 128B 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.
SpecificationMistral Medium 3.5 128BQwen3.6-27B
Context window
Maximum documented context; output-token limits may be lower.
Mistral Medium 3.5 128B
256K
Qwen3.6-27B
262K
API model ID
Mistral Medium 3.5 128B
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 Medium 3.5 128B
Not published
Qwen3.6-27B
No comparable hosted API rate
Documented inputs
Mistral Medium 3.5 128B
Not sourced
Qwen3.6-27B
Not sourced
Documented outputs
Mistral Medium 3.5 128B
Not sourced
Qwen3.6-27B
Not sourced
Provider availability
Mistral Medium 3.5 128B
Not sourced
Qwen3.6-27B
Not sourced
Reasoning profile
Mistral Medium 3.5 128B
Reasoning
Qwen3.6-27B
Reasoning
Weight access
Mistral Medium 3.5 128B
Open Weight
Qwen3.6-27B
Open Weight
License
Mistral Medium 3.5 128B
Open Weight
Qwen3.6-27B
Open Weight
Release date
Mistral Medium 3.5 128B
2026-04-29
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
- Qwen3.6-27B has the higher public score, 47.69 versus 30.13, and the 90% score intervals do not overlap.
- 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 Medium 3.5 128B
API / mo$6,750
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Qwen3.6-27B
API / mo$0
Self-host / mo$429
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 evidence43 rows
Agentic
τ³-bench results
Mistral Medium 3.5 128B91.4%SourceQwen3.6-27B—Not directly comparable
Gert Labs
Shared sourceMistral Medium 3.5 128B39.10%Qwen3.6-27B54.84%Qwen3.6-27B leads this result
Terminal-Bench 2.1 (Vals)
Mistral Medium 3.5 128B39.0%SourceQwen3.6-27B—Not directly comparable
Terminal-Bench 2.0
Mistral Medium 3.5 128B—Qwen3.6-27B59.3%SourceNot directly comparable
Claw-Eval
Mistral Medium 3.5 128B—Qwen3.6-27B72.4%SourceNot directly comparable
QwenClawBench
Mistral Medium 3.5 128B—Qwen3.6-27B53.4%SourceNot directly comparable
QwenWebBench
Mistral Medium 3.5 128B—Qwen3.6-27B1487SourceNot directly comparable
AndroidWorld
Mistral Medium 3.5 128B—Qwen3.6-27B70.3%SourceNot directly comparable
Coding
SWE-bench Verified
Mistral Medium 3.5 128B77.6%SourceQwen3.6-27B77.2%SourceMistral Medium 3.5 128B leads this result
SWE-bench (Vals)
Mistral Medium 3.5 128B66.4%SourceQwen3.6-27B—Not directly comparable
SWE Multilingual
Mistral Medium 3.5 128B—Qwen3.6-27B71.3%SourceNot directly comparable
SWE-bench Pro
Mistral Medium 3.5 128B—Qwen3.6-27B53.5%SourceNot directly comparable
Terminal-Bench 2.0
Mistral Medium 3.5 128B—Qwen3.6-27B59.3%SourceNot directly comparable
LiveCodeBench
Mistral Medium 3.5 128B—Qwen3.6-27B83.9%SourceNot directly comparable
NL2Repo
Mistral Medium 3.5 128B—Qwen3.6-27B36.2%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
Mistral Medium 3.5 128B34.8%SourceQwen3.6-27B—Not directly comparable
MMLU-Pro (Vals)
Mistral Medium 3.5 128B75.3%SourceQwen3.6-27B—Not directly comparable
MMLU-Pro
Mistral Medium 3.5 128B—Qwen3.6-27B86.2%SourceNot directly comparable
MMLU-Redux
Mistral Medium 3.5 128B—Qwen3.6-27B93.5%SourceNot directly comparable
SuperGPQA
Mistral Medium 3.5 128B—Qwen3.6-27B66%SourceNot directly comparable
C-Eval
Mistral Medium 3.5 128B—Qwen3.6-27B91.4%SourceNot directly comparable
GPQA
Mistral Medium 3.5 128B—Qwen3.6-27B87.8%SourceNot directly comparable
HLE
Mistral Medium 3.5 128B—Qwen3.6-27B24%SourceNot directly comparable
Math
HMMT Feb 2025
Mistral Medium 3.5 128B—Qwen3.6-27B93.8%SourceNot directly comparable
HMMT Nov 2025
Mistral Medium 3.5 128B—Qwen3.6-27B90.7%SourceNot directly comparable
HMMT Feb 2026
Mistral Medium 3.5 128B—Qwen3.6-27B84.3%SourceNot directly comparable
MMAnswerBench
Mistral Medium 3.5 128B—Qwen3.6-27B80.8%SourceNot directly comparable
AIME26
Mistral Medium 3.5 128B—Qwen3.6-27B94.1%SourceNot directly comparable
Multimodal
MMMU
Mistral Medium 3.5 128B—Qwen3.6-27B82.9%SourceNot directly comparable
MMMU-Pro
Mistral Medium 3.5 128B—Qwen3.6-27B75.8%SourceNot directly comparable
RealWorldQA
Mistral Medium 3.5 128B—Qwen3.6-27B84.1%SourceNot directly comparable
DynaMath
Mistral Medium 3.5 128B—Qwen3.6-27B85.6%SourceNot directly comparable
MStar
Mistral Medium 3.5 128B—Qwen3.6-27B81.4%SourceNot directly comparable
SimpleVQA
Mistral Medium 3.5 128B—Qwen3.6-27B56.1%SourceNot directly comparable
CharXiv
Mistral Medium 3.5 128B—Qwen3.6-27B78.4%SourceNot directly comparable
CC-OCR
Mistral Medium 3.5 128B—Qwen3.6-27B81.2%SourceNot directly comparable
CountBench
Mistral Medium 3.5 128B—Qwen3.6-27B97.8%SourceNot directly comparable
RefCOCO (avg)
Mistral Medium 3.5 128B—Qwen3.6-27B92.5%SourceNot directly comparable
ERQA
Mistral Medium 3.5 128B—Qwen3.6-27B62.5%SourceNot directly comparable
Video-MME (with subtitle)
Mistral Medium 3.5 128B—Qwen3.6-27B87.7%SourceNot directly comparable
VideoMMMU
Mistral Medium 3.5 128B—Qwen3.6-27B84.4%SourceNot directly comparable
MLVU (M-Avg)
Mistral Medium 3.5 128B—Qwen3.6-27B86.6%SourceNot directly comparable
V*
Mistral Medium 3.5 128B—Qwen3.6-27B94.7%SourceNot directly comparable
Frequently asked questions
Which is better, Mistral Medium 3.5 128B or Qwen3.6-27B?
Qwen3.6-27B has the higher public score, 47.69 versus 30.13, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Mistral Medium 3.5 128B or Qwen3.6-27B?
Qwen3.6-27B scores higher for coding on the public lane, 41.7 to 36.9. Mistral Medium 3.5 128B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Mistral Medium 3.5 128B or Qwen3.6-27B?
Qwen3.6-27B leads the public agentic tasks lane, 30.9 to 21.9, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Mistral Medium 3.5 128B 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 Medium 3.5 128B or Qwen3.6-27B?
Qwen3.6-27B has the larger documented context window: 262K, compared with 256K.
Related comparisons
Last updated September 10, 2026
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