Mistral Nemo vs Qwen3.7 Plus
Updated October 21, 2026. 2 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only Qwen3.7 Plus has a coding score, 33.2 on the Vibe Coding Index; Mistral Nemo has none, so the two cannot be ranked on coding.
Mistral Nemo has the edge where output tokens cost 8.5x less.
- Shared results
- 2
- Mistral Nemo only
- 1
- Qwen3.7 Plus only
- 73
- Like-for-like categories
- 2 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
No call
Only Qwen3.7 Plus has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Qwen3.7 Plus has a AA Intelligence Index result, 25.2.
Directional only
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Plus has a AA Agentic Index result, 17.5.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3.7 Plus has a Design Arena: websites result, 1269.
Directional only
Long documents
Listed context window
Qwen3.7 Plus
Qwen3.7 Plus takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Nemo
Mistral Nemo $0.0002, Qwen3.7 Plus $0.0010 per turn.
List prices
At a glance
- Coding index
- Mistral Nemo not benchmarked, Qwen3.7 Plus 33.2 from 2 benchmarks.
- Widest category
- Intelligence, 35.4 points apart on average over 1 shared benchmark.
- Widest single gap
- Epoch Capabilities Index: Mistral Nemo 118.7 against Qwen3.7 Plus 147.0, 35.4 points apart on a 0 to 100 scale.
- Output price
- Mistral Nemo $0.15 against Qwen3.7 Plus $1.28 per million output tokens, 8.5x apart.
- Context window
- Mistral Nemo takes 131K tokens, Qwen3.7 Plus 1M.
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 Nemo | Bars | Qwen3.7 Plus | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 2 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index10 boards, none shared
| Benchmark | Mistral Nemo | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 55.9 | Only Qwen3.7 Plus measured |
| Vibe Code Bench (Vals AI) | Not measured | 46.4% | Only Qwen3.7 Plus measured |
| Code migration (Vals AI) | Not measured | 12.9% | Only Qwen3.7 Plus measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 52.8% | Only Qwen3.7 Plus measured |
| SkillsBench (Vals AI) | Not measured | 54.3% | Only Qwen3.7 Plus measured |
| SciCode | Not measured | 46.1% | Only Qwen3.7 Plus measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 47.0% | Only Qwen3.7 Plus measured |
| FrontierCode | Not measured | 10.2% | Only Qwen3.7 Plus measured |
| LMArena Text: coding | Not measured | 1473 | Only Qwen3.7 Plus measured |
| LMArena Text: software and IT | Not measured | 1473 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Nemo | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 25.2 | Only Qwen3.7 Plus measured |
| Epoch Capabilities Index | 118.7 | 147.0 | Qwen3.7 Plus |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Mistral Nemo | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 17.5 | Only Qwen3.7 Plus measured |
| Finance Agent (Vals AI) | Not measured | 38.2% | Only Qwen3.7 Plus measured |
| Tax Agent Bench (Vals AI) | Not measured | 10.5% | Only Qwen3.7 Plus measured |
| Legal Research Bench (Vals AI) | Not measured | 16.4% | Only Qwen3.7 Plus measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 49.3% | Only Qwen3.7 Plus measured |
| τ²-Bench Telecom | Not measured | 93.0% | Only Qwen3.7 Plus measured |
| OSWorld 2.0 | Not measured | 2.8% | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Mistral Nemo | Qwen3.7 Plus | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 90.0% | Only Qwen3.7 Plus measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 35.6% | Only Qwen3.7 Plus measured |
| GPQA Diamond (Epoch AI run) | Not measured | 87.9% | Only Qwen3.7 Plus measured |
| Mystery game puzzles | Not measured | 17.0% | Only Qwen3.7 Plus measured |
| LMArena Text: hard prompts | Not measured | 1460 | Only Qwen3.7 Plus measured |
| LMArena Text: expert | Not measured | 1466 | Only Qwen3.7 Plus measured |
| LMArena Text: hard prompts in English | Not measured | 1460 | Only Qwen3.7 Plus measured |
| LMArena Vision: diagrams | Not measured | 1292 | Only Qwen3.7 Plus measured |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Mistral Nemo | Qwen3.7 Plus | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 93.3% | Only Qwen3.7 Plus measured |
| FrontierMath, tiers 1 to 3 | Not measured | 34.4% | Only Qwen3.7 Plus measured |
| LMArena Text: math | Not measured | 1467 | Only Qwen3.7 Plus measured |
| LMArena Text: mathematical work | Not measured | 1470 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Mistral Nemo | Qwen3.7 Plus | Leader |
|---|---|---|---|
| MortgageTax (Vals AI) | Not measured | 66.2% | Only Qwen3.7 Plus measured |
| MMLU | 65.3% | Not measured | Only Mistral Nemo measured |
| LMArena Text: science | Not measured | 1464 | Only Qwen3.7 Plus measured |
| LMArena Text: medicine and healthcare | Not measured | 1464 | Only Qwen3.7 Plus measured |
| LMArena Text: legal and government | Not measured | 1451 | Only Qwen3.7 Plus measured |
| LMArena Text: business and finance | Not measured | 1435 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Mistral Nemo | Qwen3.7 Plus | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1269 | Only Qwen3.7 Plus measured |
| Design Arena: UI components | Not measured | 1261 | Only Qwen3.7 Plus measured |
| Design Arena: game dev | Not measured | 1262 | Only Qwen3.7 Plus measured |
| Design Arena: data viz | Not measured | 1275 | Only Qwen3.7 Plus measured |
| Design Arena: 3D | Not measured | 1239 | Only Qwen3.7 Plus measured |
| Design Arena: all code categories | Not measured | 1269 | Only Qwen3.7 Plus measured |
| Design Arena: SVG | Not measured | 1208 | Only Qwen3.7 Plus measured |
| Design Arena: ASCII art | Not measured | 1144 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Nemo | Qwen3.7 Plus | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 39.3% | Only Qwen3.7 Plus measured |
| Vals Multimodal Index | Not measured | 53.9% | Only Qwen3.7 Plus measured |
| IFBench | Not measured | 78.0% | Only Qwen3.7 Plus measured |
| Long context reasoning (AA-LCR) | Not measured | 73.0% | Only Qwen3.7 Plus measured |
| DTBench | 48.6% | 84.0% | Qwen3.7 Plus |
| LMCA | Not measured | 37.6% | Only Qwen3.7 Plus measured |
| LMArena Text | Not measured | 1454 | Only Qwen3.7 Plus measured |
| LMArena Text: instruction following | Not measured | 1439 | Only Qwen3.7 Plus measured |
| LMArena Text: creative writing | Not measured | 1438 | Only Qwen3.7 Plus measured |
| LMArena Vision | Not measured | 1278 | Only Qwen3.7 Plus measured |
| LMArena Text: English | Not measured | 1458 | Only Qwen3.7 Plus measured |
| LMArena Text: non-English | Not measured | 1445 | Only Qwen3.7 Plus measured |
| LMArena Text: Chinese | Not measured | 1509 | Only Qwen3.7 Plus measured |
| LMArena Text: German | Not measured | 1471 | Only Qwen3.7 Plus measured |
| LMArena Text: Japanese | Not measured | 1412 | Only Qwen3.7 Plus measured |
| LMArena Text: Korean | Not measured | 1413 | Only Qwen3.7 Plus measured |
| LMArena Text: French | Not measured | 1472 | Only Qwen3.7 Plus measured |
| LMArena Text: Spanish | Not measured | 1457 | Only Qwen3.7 Plus measured |
| LMArena Text: Russian | Not measured | 1457 | Only Qwen3.7 Plus measured |
| LMArena Text: Polish | Not measured | 1449 | Only Qwen3.7 Plus measured |
| LMArena Text: multi-turn | Not measured | 1459 | Only Qwen3.7 Plus measured |
| LMArena Text: longer queries | Not measured | 1454 | Only Qwen3.7 Plus measured |
| LMArena Text: writing and language | Not measured | 1437 | Only Qwen3.7 Plus measured |
| LMArena Text: entertainment and media | Not measured | 1422 | Only Qwen3.7 Plus measured |
| LMArena Vision: English | Not measured | 1280 | Only Qwen3.7 Plus measured |
| LMArena Vision: Chinese | Not measured | 1332 | Only Qwen3.7 Plus measured |
| LMArena Vision: reading text | Not measured | 1288 | Only Qwen3.7 Plus measured |
| LMArena Vision: humor | Not measured | 1261 | Only Qwen3.7 Plus measured |
| LMArena Vision: creative writing | Not measured | 1258 | Only Qwen3.7 Plus measured |
| LMArena Document | Not measured | 1444 | Only Qwen3.7 Plus 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 Nemo | Qwen3.7 Plus | 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
Mistral Nemo costs less
Repository review
50K input + 3K output tokens
Mistral Nemo costs less
Agent loop
200K input + 10K output tokens
Mistral Nemo 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 Nemo | Qwen3.7 Plus |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | Open weights | No public weights listed |
| Released | July 19, 2024 | June 3, 2026 |
| Context window | 131K | 1M |
| Input $/Mtok | $0.15 | $0.32 |
| Output $/Mtok | $0.15 | $1.28 |
| Coding index | Not benchmarked | 33.2 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Nemo better than Qwen3.7 Plus for coding?
Only Qwen3.7 Plus has a coding score, 33.2 on the Vibe Coding Index; Mistral Nemo has none, so the two cannot be ranked on coding. Mistral Nemo has the edge where output tokens cost 8.5x less.
Which is cheaper, Mistral Nemo or Qwen3.7 Plus?
Mistral Nemo costs $0.15 per million input tokens and $0.15 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Nemo or Qwen3.7 Plus?
Qwen3.7 Plus, with 1M tokens against 131K.
How many benchmarks is the Mistral Nemo vs Qwen3.7 Plus comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. Mistral Nemo's coding index rests on 0 independent benchmarks and Qwen3.7 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.