DeepSeek V3.1 vs Mistral Small 3.2 24B
Mistral Small 3.2 24B costs 5.0x less per output token than DeepSeek V3.1 ($0.30 vs $1.50 per million). Updated October 22, 2026; 5 benchmarks have a result for both.
DeepSeek V3.1 or Mistral Small 3.2 24B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 5
- DeepSeek V3.1 only
- 6
- Mistral Small 3.2 24B only
- 0
- Like-for-like categories
- 1 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 V3.1 has a AA Intelligence Index result, 13.7.
Directional only
Web design
Design Arena: websites
DeepSeek V3.1
DeepSeek V3.1 leads, 1122 against 896.
Same board, both measured
Long documents
Listed context window
Mistral Small 3.2 24B
Mistral Small 3.2 24B takes 256K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3.2 24B
DeepSeek V3.1 $0.0013, Mistral Small 3.2 24B $0.0003 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, Mistral Small 3.2 24B not benchmarked.
- Widest category
- Web design, 24.3 points apart on average over 5 shared benchmarks.
- Widest single gap
- Design Arena: websites: DeepSeek V3.1 1122 against Mistral Small 3.2 24B 896, 28.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against Mistral Small 3.2 24B $0.30 per million output tokens, 5.0x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, Mistral Small 3.2 24B 256K.
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 V3.1 | Bars | Mistral Small 3.2 24B | Ties |
|---|---|---|---|---|
| Web design | 5 | 0 | ||
| All shared boards | 5 | 0 |
CodingNot comparableSix of these boards feed the Vibe Coding Index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3.1 | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Neither model has a result on 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3.1 | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Kagi LLM Benchmark | 53.2% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 21 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V3.1 | Mistral Small 3.2 24B | Leader |
|---|---|---|---|
| Design Arena: websites | 1122 | 896 | DeepSeek V3.1 |
| Design Arena: UI components | 1085 | 907 | DeepSeek V3.1 |
| Design Arena: game dev | 1084 | 889 | DeepSeek V3.1 |
| Design Arena: data viz | 1096 | 929 | DeepSeek V3.1 |
| Design Arena: 3D | 1077 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: all code categories | 1112 | 907 | DeepSeek V3.1 |
| Design Arena: SVG | 967 | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Mistral Small 3.2 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 3.2 24B costs less
Repository review
50K input + 3K output tokens
Mistral Small 3.2 24B costs less
Agent loop
200K input + 10K output tokens
Mistral Small 3.2 24B 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 V3.1 | Mistral Small 3.2 24B |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | Open weights |
| Released | August 21, 2025 | June 20, 2025 |
| Context window | 164K | 256K |
| Input $/Mtok | $0.55 | $0.10 |
| Output $/Mtok | $1.50 | $0.30 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 better than Mistral Small 3.2 24B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, DeepSeek V3.1 or Mistral Small 3.2 24B?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; Mistral Small 3.2 24B costs $0.10 and $0.30. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or Mistral Small 3.2 24B?
Mistral Small 3.2 24B, with 256K tokens against 164K.
How many benchmarks is the DeepSeek V3.1 vs Mistral Small 3.2 24B comparison based on?
5 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and Mistral Small 3.2 24B's on 0. Where only one model has a result, the page says so instead of naming a winner.