DeepSeek V3.1 vs Mistral Large 2407
DeepSeek V3.1 leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 13.7 to 7.6, and DeepSeek V3.1 costs 4.0x less per output token ($1.50 vs $6.00 per million). Updated October 21, 2026; 2 benchmarks have a result for both.
DeepSeek V3.1 or Mistral Large 2407: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for output tokens 4.0x cheaper ($1.50 vs $6.00 per million) and a larger context window (164K against 131K tokens).
- Shared results
- 2
- DeepSeek V3.1 only
- 9
- Mistral Large 2407 only
- 15
- 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
DeepSeek V3.1
DeepSeek V3.1 leads, 13.7 against 7.6.
Same board, both measured
Web design
Design Arena: websites
No call
Only DeepSeek V3.1 has a Design Arena: websites result, 1123.
Directional only
Long documents
Listed context window
DeepSeek V3.1
DeepSeek V3.1 takes 164K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.1
DeepSeek V3.1 $0.0013, Mistral Large 2407 $0.0050 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- General, 21.5 points apart on average over 1 shared benchmark.
- Widest single gap
- DTBench: DeepSeek V3.1 82.7% against Mistral Large 2407 61.2%, 21.5 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against Mistral Large 2407 $6.00 per million output tokens, 4.0x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, Mistral Large 2407 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 V3.1 | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board, none shared
| Benchmark | DeepSeek V3.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 26.7% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 7.6 | DeepSeek V3.1 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 33.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 47.2% | Only Mistral Large 2407 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 2.9% | Only Mistral Large 2407 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 49.0% | Only Mistral Large 2407 measured |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 measured |
| Kagi LLM Benchmark | 53.2% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 0.0 | Only Mistral Large 2407 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 0.0% | Only Mistral Large 2407 measured |
| OTIS Mock AIME | Not measured | 8.5% | Only Mistral Large 2407 measured |
| MATH level 5 | Not measured | 44.8% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 68.3% | Only Mistral Large 2407 measured |
| MMLU | Not measured | 80.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| Design Arena: websites | 1123 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: UI components | 1085 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: game dev | 1084 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: data viz | 1095 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: 3D | 1077 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: all code categories | 1112 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: SVG | 967 | Not measured | Only DeepSeek V3.1 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 V3.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| IFBench | Not measured | 31.6% | Only Mistral Large 2407 measured |
| Long context reasoning (AA-LCR) | Not measured | 2.0% | Only Mistral Large 2407 measured |
| DTBench | 82.7% | 61.2% | DeepSeek V3.1 |
| LMCA | 24.3% | Not measured | Only DeepSeek V3.1 measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Mistral Large 2407 | 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
DeepSeek V3.1 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.1 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.1 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 Large 2407 |
|---|---|---|
| Maker | DeepSeek | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | August 21, 2025 | November 19, 2024 |
| Context window | 164K | 131K |
| Input $/Mtok | $0.55 | $2.00 |
| Output $/Mtok | $1.50 | $6.00 |
| 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 Large 2407 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for output tokens 4.0x cheaper ($1.50 vs $6.00 per million) and a larger context window (164K against 131K tokens).
Which is cheaper, DeepSeek V3.1 or Mistral Large 2407?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; Mistral Large 2407 costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or Mistral Large 2407?
DeepSeek V3.1, with 164K tokens against 131K.
How many benchmarks is the DeepSeek V3.1 vs Mistral Large 2407 comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and Mistral Large 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.