DeepSeek V3.2 Exp vs Mistral Nemo
Mistral Nemo costs 2.7x less per output token than DeepSeek V3.2 Exp ($0.15 vs $0.41 per million). Updated October 21, 2026; 2 benchmarks have a result for both.
DeepSeek V3.2 Exp or Mistral Nemo: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.2 Exp for a larger context window (164K against 131K tokens).
- Shared results
- 2
- DeepSeek V3.2 Exp only
- 55
- Mistral Nemo only
- 1
- Like-for-like categories
- 2 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
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only DeepSeek V3.2 Exp has a AA Intelligence Index result, 16.6.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 Exp has a Design Arena: websites result, 1179.
Directional only
Long documents
Listed context window
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp takes 164K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Nemo
DeepSeek V3.2 Exp $0.0005, Mistral Nemo $0.0002 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, Mistral Nemo not benchmarked.
- Widest category
- General, 39.1 points apart on average over 1 shared benchmark.
- Widest single gap
- DTBench: DeepSeek V3.2 Exp 87.7% against Mistral Nemo 48.6%, 39.1 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against Mistral Nemo $0.15 per million output tokens, 2.7x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, Mistral Nemo 131K.
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 | DeepSeek V3.2 Exp | Bars | Mistral Nemo | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Mistral Nemo | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev | 1272 | Not measured | Only DeepSeek V3.2 Exp measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| WeirdML | 39.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: coding | 1438 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: software and IT | 1438 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev: HTML | 1281 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.2 Exp | Mistral Nemo | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | Not measured | Only DeepSeek V3.2 Exp measured |
| Epoch Capabilities Index | 144.9 | 118.7 | DeepSeek V3.2 Exp |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Mistral Nemo | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Mistral Nemo | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: hard prompts | 1429 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: expert | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: hard prompts in English | 1444 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Mistral Nemo | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only DeepSeek V3.2 Exp measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: math | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: mathematical work | 1433 | Not measured | Only DeepSeek V3.2 Exp 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 | DeepSeek V3.2 Exp | Mistral Nemo | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| MMLU | Not measured | 65.3% | Only Mistral Nemo measured |
| LMArena Text: science | 1443 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: medicine and healthcare | 1468 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: legal and government | 1419 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: business and finance | 1412 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Mistral Nemo | Leader |
|---|---|---|---|
| Design Arena: websites | 1179 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: UI components | 1167 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: game dev | 1144 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: data viz | 1152 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: 3D | 1148 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: all code categories | 1171 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: SVG | 1032 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.2 Exp | Mistral Nemo | Leader |
|---|---|---|---|
| IFBench | 54.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Long context reasoning (AA-LCR) | 72.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| DTBench | 87.7% | 48.6% | DeepSeek V3.2 Exp |
| LMCA | 29.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Fiction.LiveBench | 83.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: instruction following | 1401 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: creative writing | 1404 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: English | 1441 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: non-English | 1404 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Chinese | 1461 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: German | 1440 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Japanese | 1331 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Korean | 1372 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: French | 1434 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Spanish | 1441 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Russian | 1412 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Polish | 1435 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: multi-turn | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: longer queries | 1418 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: writing and language | 1407 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: entertainment and media | 1407 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Mistral Nemo | 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 | DeepSeek V3.2 Exp | Mistral Nemo |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | Open weights |
| Released | September 29, 2025 | July 19, 2024 |
| Context window | 164K | 131K |
| Input $/Mtok | $0.27 | $0.15 |
| Output $/Mtok | $0.41 | $0.15 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than Mistral Nemo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for a larger context window (164K against 131K tokens).
Which is cheaper, DeepSeek V3.2 Exp or Mistral Nemo?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Mistral Nemo costs $0.15 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.2 Exp or Mistral Nemo?
DeepSeek V3.2 Exp, with 164K tokens against 131K.
How many benchmarks is the DeepSeek V3.2 Exp vs Mistral Nemo comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and Mistral Nemo's on 0. Where only one model has a result, the page says so instead of naming a winner.