Mistral Medium 3.5 vs Qwen3.6 27B
Qwen3.6 27B leads Mistral Medium 3.5 on coding, 36.8 to 25.5 on the Vibe Coding Index, and Qwen3.6 27B costs 2.8x less per output token ($2.70 vs $7.50 per million). Updated October 22, 2026; 20 benchmarks have a result for both.
Mistral Medium 3.5 or Qwen3.6 27B: which to pick
Pick Qwen3.6 27B for coding: it scores 36.8 on the Vibe Coding Index against 25.5 for Mistral Medium 3.5, though on 2 benchmarks against 3.
Pick Mistral Medium 3.5 for work measured by IFBench, where it leads.
- Shared results
- 20
- Mistral Medium 3.5 only
- 61
- Qwen3.6 27B only
- 4
- Like-for-like categories
- 6 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
Qwen3.6 27B
Qwen3.6 27B scores 36.8 against 25.5.
3 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.6 27B
Qwen3.6 27B leads, 21.4 against 14.2.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.6 27B
Qwen3.6 27B leads, 18.5 against 7.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Mistral Medium 3.5 has a GPQA Diamond result, 34.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Medium 3.5 has a MMLU Pro result, 75.3%.
Directional only
Web design
Design Arena: websites
No call
Only Mistral Medium 3.5 has a Design Arena: websites result, 1142.
Directional only
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 27B
Mistral Medium 3.5 $0.0053, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Qwen3.6 27B
Mistral Medium 3.5 $6.02, Qwen3.6 27B $1.30.
Published attempt cost and pass rate
At a glance
- Coding index
- Mistral Medium 3.5 25.5 from 3 benchmarks, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Knowledge, 13.8 points apart on average over 4 shared benchmarks.
- Widest single gap
- MortgageTax (Vals AI): Mistral Medium 3.5 28.9% against Qwen3.6 27B 68.3%, 39.4 points apart on a 0 to 100 scale.
- Output price
- Mistral Medium 3.5 $7.50 against Qwen3.6 27B $2.70 per million output tokens, 2.8x apart.
- Context window
- Both take 262K tokens.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Mistral Medium 3.5 | Bars | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | 1 tie | |
| Reasoning | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 1 | 4 | ||
| All shared boards | 1 | 18 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | Mistral Medium 3.5 | Qwen3.6 27B | Leader |
|---|---|---|---|
| LMArena WebDev | 1264 | Not measured | Only Mistral Medium 3.5 measured |
| AA Coding Index | 46.9 | 53.7 | Qwen3.6 27B |
| SWE-bench Verified (Vals AI) | 66.4% | 70.0% | Qwen3.6 27B |
| Vibe Code Bench (Vals AI) | 2.9% | 11.9% | Qwen3.6 27B |
| Code migration (Vals AI) | 5.1% | Not measured | Only Mistral Medium 3.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 39.0% | Not measured | Only Mistral Medium 3.5 measured |
| SciCode | 40.2% | 42.8% | Qwen3.6 27B |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 34.8% | Qwen3.6 27B |
| ALE-Bench | 764.0 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: coding | 1460 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: software and IT | 1458 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena WebDev: React | 1254 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena WebDev: HTML | 1308 | Not measured | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Medium 3.5 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.2 | 21.4 | Qwen3.6 27B |
| Epoch Capabilities Index | 141.4 | 146.2 | Qwen3.6 27B |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Medium 3.5 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | 7.0 | 18.5 | Qwen3.6 27B |
| Finance Agent (Vals AI) | 32.1% | Not measured | Only Mistral Medium 3.5 measured |
| Tax Agent Bench (Vals AI) | 4.5% | Not measured | Only Mistral Medium 3.5 measured |
| Legal Research Bench (Vals AI) | 9.1% | Not measured | Only Mistral Medium 3.5 measured |
| Excel Modeling Benchmark (Vals AI) | 11.2% | Not measured | Only Mistral Medium 3.5 measured |
| τ²-Bench Telecom | 94.2% | 94.2% | Tie |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Medium 3.5 | Qwen3.6 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 34.9% | Not measured | Only Mistral Medium 3.5 measured |
| GPQA Diamond (Artificial Analysis run) | 74.8% | 84.2% | Qwen3.6 27B |
| Humanity's Last Exam (Artificial Analysis run) | 13.8% | 23.1% | Qwen3.6 27B |
| GPQA Diamond (Epoch AI run) | Not measured | 85.9% | Only Qwen3.6 27B measured |
| Mystery game puzzles | Not measured | 7.0% | Only Qwen3.6 27B measured |
| LMArena Text: hard prompts | 1436 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: expert | 1432 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: hard prompts in English | 1445 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: diagrams | 1254 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: homework | 1230 | Not measured | Only Mistral Medium 3.5 measured |
| Kagi LLM Benchmark | 41.4% | Not measured | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Mistral Medium 3.5 | Qwen3.6 27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 91.1% | Only Qwen3.6 27B measured |
| FrontierMath, tiers 1 to 3 | Not measured | 35.1% | Only Qwen3.6 27B measured |
| ProofBench | 9.0% | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: math | 1431 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: mathematical work | 1440 | Not measured | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Mistral Medium 3.5 | Qwen3.6 27B | Leader |
|---|---|---|---|
| MMLU Pro | 75.3% | Not measured | Only Mistral Medium 3.5 measured |
| CaseLaw (Vals AI) | 44.2% | 53.2% | Qwen3.6 27B |
| CorpFin (Vals AI) | 58.8% | 62.3% | Qwen3.6 27B |
| TaxEval (Vals AI) | 68.0% | 71.3% | Qwen3.6 27B |
| MortgageTax (Vals AI) | 28.9% | 68.3% | Qwen3.6 27B |
| MedCode (Vals AI) | 33.8% | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: science | 1428 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: medicine and healthcare | 1409 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: legal and government | 1437 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: business and finance | 1426 | Not measured | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Mistral Medium 3.5 | Qwen3.6 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1142 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: UI components | 1076 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: game dev | 1079 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: data viz | 1120 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: 3D | 1060 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: all code categories | 1121 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: SVG | 949 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: ASCII art | 1062 | Not measured | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | Mistral Medium 3.5 | Qwen3.6 27B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 37.6% | 45.6% | Qwen3.6 27B |
| MedScribe (Vals AI) | 67.7% | Not measured | Only Mistral Medium 3.5 measured |
| Vals Multimodal Index | 34.8% | Not measured | Only Mistral Medium 3.5 measured |
| IFBench | 68.8% | 67.6% | Mistral Medium 3.5 |
| Long context reasoning (AA-LCR) | 69.3% | 77.3% | Qwen3.6 27B |
| DTBench | 75.5% | 78.1% | Qwen3.6 27B |
| LMCA | 26.1% | 34.5% | Qwen3.6 27B |
| Surface Evolver Bench | 26.9% | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text | 1421 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: instruction following | 1415 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: creative writing | 1374 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision | 1222 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: English | 1433 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: non-English | 1404 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: Chinese | 1444 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: German | 1453 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: Korean | 1384 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: French | 1448 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: Spanish | 1411 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: Russian | 1395 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: Polish | 1430 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: multi-turn | 1423 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: longer queries | 1415 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: writing and language | 1384 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: entertainment and media | 1382 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: English | 1240 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: Chinese | 1244 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: reading text | 1233 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: humor | 1216 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: creative writing | 1198 | Not measured | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Medium 3.5 | Qwen3.6 27B | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Qwen3.6 27B costs less
Repository review
50K input + 3K output tokens
Qwen3.6 27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 27B costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Mistral Medium 3.5 | Qwen3.6 27B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 30, 2026 | April 27, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $1.50 | $0.45 |
| Output $/Mtok | $7.50 | $2.70 |
| Coding index | 25.5 | 36.8 |
| Independent measurements behind it | 3 | 2 |
| Cost per solved task | $6.02 | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Medium 3.5 better than Qwen3.6 27B for coding?
Pick Qwen3.6 27B for coding: it scores 36.8 on the Vibe Coding Index against 25.5 for Mistral Medium 3.5, though on 2 benchmarks against 3. Pick Mistral Medium 3.5 for work measured by IFBench, where it leads.
Which is cheaper, Mistral Medium 3.5 or Qwen3.6 27B?
Mistral Medium 3.5 costs $1.50 per million input tokens and $7.50 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Per solved SWE-bench Verified task, Mistral Medium 3.5 costs $6.02 and Qwen3.6 27B $1.30.
Which has the bigger context window, Mistral Medium 3.5 or Qwen3.6 27B?
Neither: both take 262K tokens.
How many benchmarks is the Mistral Medium 3.5 vs Qwen3.6 27B comparison based on?
20 benchmarks have a result for both models as of October 22, 2026. Mistral Medium 3.5's coding index rests on 3 independent benchmarks and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.