DeepSeek V3.2 vs Mistral Small 3.1 24B
Mistral Small 3.1 24B costs 1.6x less per output token than DeepSeek V3.2 ($0.555 vs $0.88 per million). Updated October 22, 2026; 28 benchmarks have a result for both.
DeepSeek V3.2 or Mistral Small 3.1 24B: which to pick
Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Mistral Small 3.1 24B has none, so the two cannot be ranked on coding.
Pick Mistral Small 3.1 24B for output tokens 1.6x cheaper ($0.555 vs $0.88 per million).
- Shared results
- 28
- DeepSeek V3.2 only
- 47
- Mistral Small 3.1 24B only
- 10
- Like-for-like categories
- 5 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
Only DeepSeek V3.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only DeepSeek V3.2 has a AA Intelligence Index result, 21.5.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V3.2 has a GPQA Diamond result, 80.3%.
Directional only
Math
AIME
No call
Only DeepSeek V3.2 has a AIME result, 84.6%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V3.2 has a MMLU Pro result, 84.9%.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 has a Design Arena: websites result, 1175.
Directional only
Long documents
Listed context window
DeepSeek V3.2
DeepSeek V3.2 takes 164K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3.1 24B
DeepSeek V3.2 $0.0007, Mistral Small 3.1 24B $0.0006 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V3.2 has the number, $0.99.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 30.0 from 3 benchmarks, Mistral Small 3.1 24B not benchmarked.
- Widest category
- Math, 28.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: DeepSeek V3.2 1455 against Mistral Small 3.1 24B 1253, 33.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.88 against Mistral Small 3.1 24B $0.555 per million output tokens, 1.6x apart.
- Context window
- DeepSeek V3.2 takes 164K tokens, Mistral Small 3.1 24B 128K.
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 | Bars | Mistral Small 3.1 24B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 17 | 0 | ||
| All shared boards | 28 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | DeepSeek V3.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| SWE-bench Verified | 70.0% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Multilingual | 59.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev | 1362 | Not measured | Only DeepSeek V3.2 measured |
| AA Coding Index | 44.2 | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Vals AI) | 80.7% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Verified (Vals AI) | 67.6% | Not measured | Only DeepSeek V3.2 measured |
| Vibe Code Bench (Vals AI) | 5.1% | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 35.6% | Not measured | Only DeepSeek V3.2 measured |
| Terminal-Bench | 39.6% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: coding | 1453 | 1309 | DeepSeek V3.2 |
| LMArena Text: software and IT | 1449 | 1301 | DeepSeek V3.2 |
| LMArena WebDev: React | 1350 | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev: HTML | 1371 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | Not measured | Only DeepSeek V3.2 measured |
| Epoch Capabilities Index | 146.1 | Not measured | Only DeepSeek V3.2 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 90.6% | Not measured | Only DeepSeek V3.2 measured |
| APEX-Agents | 21.3% | Not measured | Only DeepSeek V3.2 measured |
| Vending-Bench 2 | $1,034 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V3.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | Not measured | Only DeepSeek V3.2 measured |
| GPQA Diamond (Artificial Analysis run) | 84.0% | Not measured | Only DeepSeek V3.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | Not measured | Only DeepSeek V3.2 measured |
| ARC-AGI | 57.0% | Not measured | Only DeepSeek V3.2 measured |
| ARC-AGI-2 | 4.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: hard prompts | 1434 | 1278 | DeepSeek V3.2 |
| LMArena Text: expert | 1436 | 1257 | DeepSeek V3.2 |
| LMArena Text: hard prompts in English | 1446 | 1290 | DeepSeek V3.2 |
| LMArena Vision: diagrams | Not measured | 1119 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: homework | Not measured | 1158 | Only Mistral Small 3.1 24B measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AIME | 84.6% | Not measured | Only DeepSeek V3.2 measured |
| MGSM (Vals AI) | 86.0% | Not measured | Only DeepSeek V3.2 measured |
| AA Math Index | 92.0 | Not measured | Only DeepSeek V3.2 measured |
| AIME 2025 (Artificial Analysis run) | 92.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: math | 1435 | 1261 | DeepSeek V3.2 |
| LMArena Text: mathematical work | 1440 | 1271 | DeepSeek V3.2 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| MMLU Pro | 84.9% | Not measured | Only DeepSeek V3.2 measured |
| LegalBench (Vals AI) | 76.1% | Not measured | Only DeepSeek V3.2 measured |
| CaseLaw (Vals AI) | 55.4% | Not measured | Only DeepSeek V3.2 measured |
| CorpFin (Vals AI) | 51.0% | Not measured | Only DeepSeek V3.2 measured |
| TaxEval (Vals AI) | 68.2% | Not measured | Only DeepSeek V3.2 measured |
| MedQA (Vals AI) | 93.9% | Not measured | Only DeepSeek V3.2 measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: science | 1446 | 1265 | DeepSeek V3.2 |
| LMArena Text: medicine and healthcare | 1440 | 1267 | DeepSeek V3.2 |
| LMArena Text: legal and government | 1425 | 1271 | DeepSeek V3.2 |
| LMArena Text: business and finance | 1410 | 1257 | DeepSeek V3.2 |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V3.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| Design Arena: websites | 1175 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: UI components | 1146 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: game dev | 1131 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: data viz | 1158 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: 3D | 1132 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: all code categories | 1163 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: SVG | 1032 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: ASCII art | 1083 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
| Benchmark | DeepSeek V3.2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| IFBench | 60.7% | Not measured | Only DeepSeek V3.2 measured |
| Long context reasoning (AA-LCR) | 73.3% | Not measured | Only DeepSeek V3.2 measured |
| DTBench | 85.6% | Not measured | Only DeepSeek V3.2 measured |
| LMCA | 28.8% | Not measured | Only DeepSeek V3.2 measured |
| CL-bench | 12.4% | Not measured | Only DeepSeek V3.2 measured |
| CL-bench Life | 7.4% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text | 1425 | 1277 | DeepSeek V3.2 |
| LMArena Text: instruction following | 1413 | 1263 | DeepSeek V3.2 |
| LMArena Text: creative writing | 1400 | 1252 | DeepSeek V3.2 |
| LMArena Vision | Not measured | 1136 | Only Mistral Small 3.1 24B measured |
| LMArena Text: English | 1439 | 1295 | DeepSeek V3.2 |
| LMArena Text: non-English | 1409 | 1255 | DeepSeek V3.2 |
| LMArena Text: Chinese | 1455 | 1253 | DeepSeek V3.2 |
| LMArena Text: German | 1413 | 1266 | DeepSeek V3.2 |
| LMArena Text: Japanese | 1374 | 1208 | DeepSeek V3.2 |
| LMArena Text: Korean | 1370 | 1207 | DeepSeek V3.2 |
| LMArena Text: French | 1429 | 1273 | DeepSeek V3.2 |
| LMArena Text: Spanish | 1424 | 1284 | DeepSeek V3.2 |
| LMArena Text: Russian | 1424 | 1263 | DeepSeek V3.2 |
| LMArena Text: Polish | 1419 | 1242 | DeepSeek V3.2 |
| LMArena Text: multi-turn | 1427 | 1270 | DeepSeek V3.2 |
| LMArena Text: longer queries | 1428 | 1299 | DeepSeek V3.2 |
| LMArena Text: writing and language | 1407 | 1268 | DeepSeek V3.2 |
| LMArena Text: entertainment and media | 1395 | 1250 | DeepSeek V3.2 |
| LMArena Vision: English | Not measured | 1157 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: Chinese | Not measured | 1155 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: reading text | Not measured | 1132 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: humor | Not measured | 1142 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: captioning | Not measured | 1153 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: recognizing things | Not measured | 1155 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: creative writing | Not measured | 1123 | Only Mistral Small 3.1 24B measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 | Mistral Small 3.1 24B | 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 Small 3.1 24B costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 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 | Mistral Small 3.1 24B |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | Open weights |
| Released | December 1, 2025 | March 17, 2025 |
| Context window | 164K | 128K |
| Input $/Mtok | $0.29 | $0.351 |
| Output $/Mtok | $0.88 | $0.555 |
| Coding index | 30.0 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.99 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 better than Mistral Small 3.1 24B for coding?
Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Mistral Small 3.1 24B has none, so the two cannot be ranked on coding. Pick Mistral Small 3.1 24B for output tokens 1.6x cheaper ($0.555 vs $0.88 per million).
Which is cheaper, DeepSeek V3.2 or Mistral Small 3.1 24B?
DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Mistral Small 3.1 24B costs $0.351 and $0.555. Cost per solved task is known for DeepSeek V3.2 only.
Which has the bigger context window, DeepSeek V3.2 or Mistral Small 3.1 24B?
DeepSeek V3.2, with 164K tokens against 128K.
How many benchmarks is the DeepSeek V3.2 vs Mistral Small 3.1 24B comparison based on?
28 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and Mistral Small 3.1 24B's on 0. Where only one model has a result, the page says so instead of naming a winner.