DeepSeek V3 vs Mistral Small 4
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
Neither model has a coding score yet, so the two cannot be ranked on coding.
DeepSeek V3 has the edge where what matters is DTBench or LMCA, where it leads.
- Shared results
- 2
- DeepSeek V3 only
- 11
- Mistral Small 4 only
- 12
- Like-for-like categories
- 1 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Mistral Small 4 has a AA Intelligence Index result, 11.3.
Directional only
Agentic work
AA Agentic Index
No call
Only Mistral Small 4 has a AA Agentic Index result, 0.8.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3 has a Design Arena: websites result, 1120.
Directional only
Long documents
Listed context window
Mistral Small 4
Mistral Small 4 takes 262K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 4
DeepSeek V3 $0.0008, Mistral Small 4 $0.0004 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3 not benchmarked, Mistral Small 4 not benchmarked.
- Widest category
- General, 6.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- DTBench: DeepSeek V3 62.7% against Mistral Small 4 70.9%, 8.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 $0.9594 against Mistral Small 4 $0.60 per million output tokens, 1.6x apart.
- Context window
- DeepSeek V3 takes 164K tokens, Mistral Small 4 262K.
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 | Bars | Mistral Small 4 | Ties |
|---|---|---|---|---|
| General | 0 | 2 | ||
| All shared boards | 0 | 2 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | DeepSeek V3 | Mistral Small 4 | Leader |
|---|---|---|---|
| Aider polyglot | 70.2% | Not measured | Only DeepSeek V3 measured |
| SWE-bench Lite | 30.7% | Not measured | Only DeepSeek V3 measured |
| AA Coding Index | Not measured | 26.6 | Only Mistral Small 4 measured |
| Vibe Code Bench (Vals AI) | Not measured | 0.0% | Only Mistral Small 4 measured |
| SciCode | Not measured | 38.8% | Only Mistral Small 4 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 17.4% | Only Mistral Small 4 measured |
| ALE-Bench | Not measured | 497.6 | Only Mistral Small 4 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3 | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 11.3 | Only Mistral Small 4 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3 | Mistral Small 4 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.8 | Only Mistral Small 4 measured |
| τ²-Bench Telecom | Not measured | 41.2% | Only Mistral Small 4 measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3 | Mistral Small 4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 76.9% | Only Mistral Small 4 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 9.9% | Only Mistral Small 4 measured |
| GPQA Diamond (Epoch AI run) | 71.2% | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3 | Mistral Small 4 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 48.9% | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 | Mistral Small 4 | Leader |
|---|---|---|---|
| Neither model has a result on 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3 | Mistral Small 4 | Leader |
|---|---|---|---|
| Design Arena: websites | 1120 | Not measured | Only DeepSeek V3 measured |
| Design Arena: UI components | 1093 | Not measured | Only DeepSeek V3 measured |
| Design Arena: game dev | 1053 | Not measured | Only DeepSeek V3 measured |
| Design Arena: data viz | 1089 | Not measured | Only DeepSeek V3 measured |
| Design Arena: 3D | 1087 | Not measured | Only DeepSeek V3 measured |
| Design Arena: all code categories | 1108 | Not measured | Only DeepSeek V3 measured |
| Design Arena: SVG | 977 | Not measured | Only DeepSeek V3 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3 | Mistral Small 4 | Leader |
|---|---|---|---|
| IFBench | Not measured | 48.2% | Only Mistral Small 4 measured |
| Long context reasoning (AA-LCR) | Not measured | 49.7% | Only Mistral Small 4 measured |
| DTBench | 62.7% | 70.9% | Mistral Small 4 |
| LMCA | 15.2% | 20.6% | Mistral Small 4 |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3 | Mistral Small 4 | 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 4 costs less
Repository review
50K input + 3K output tokens
Mistral Small 4 costs less
Agent loop
200K input + 10K output tokens
Mistral Small 4 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 | Mistral Small 4 |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | Open weights |
| Released | December 26, 2024 | March 16, 2026 |
| Context window | 164K | 262K |
| Input $/Mtok | $0.2887 | $0.15 |
| Output $/Mtok | $0.9594 | $0.60 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 better than Mistral Small 4 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. DeepSeek V3 has the edge where what matters is DTBench or LMCA, where it leads.
Which is cheaper, DeepSeek V3 or Mistral Small 4?
DeepSeek V3 costs $0.2887 per million input tokens and $0.9594 per million output tokens; Mistral Small 4 costs $0.15 and $0.60. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3 or Mistral Small 4?
Mistral Small 4, with 262K tokens against 164K.
How many benchmarks is the DeepSeek V3 vs Mistral Small 4 comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3's coding index rests on 0 independent benchmarks and Mistral Small 4's on 1. Where only one model has a result, the page says so instead of naming a winner.