Kimi K2 0905 vs Mistral Large 2407
Kimi K2 0905 leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 15.3 to 7.6, and Kimi K2 0905 costs 2.4x less per output token ($2.50 vs $6.00 per million). Updated October 21, 2026; 10 benchmarks have a result for both.
Kimi K2 0905 or Mistral Large 2407: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Kimi K2 0905 for output tokens 2.4x cheaper ($2.50 vs $6.00 per million) and a larger context window (262K against 131K tokens).
- Shared results
- 10
- Kimi K2 0905 only
- 7
- Mistral Large 2407 only
- 7
- Like-for-like categories
- 7 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
Kimi K2 0905
Kimi K2 0905 leads, 15.3 against 7.6.
Same board, both measured
Web design
Design Arena: websites
No call
Only Kimi K2 0905 has a Design Arena: websites result, 1089.
Directional only
Long documents
Listed context window
Kimi K2 0905
Kimi K2 0905 takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Kimi K2 0905
Kimi K2 0905 $0.0019, Mistral Large 2407 $0.0050 per turn.
List prices
At a glance
- Coding index
- Kimi K2 0905 not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- Math, 57.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- AIME 2025 (Artificial Analysis run): Kimi K2 0905 57.3% against Mistral Large 2407 0.0%, 57.3 points apart on a 0 to 100 scale.
- Output price
- Kimi K2 0905 $2.50 against Mistral Large 2407 $6.00 per million output tokens, 2.4x apart.
- Context window
- Kimi K2 0905 takes 262K 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 | Kimi K2 0905 | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 10 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Kimi K2 0905 | Mistral Large 2407 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 61.0% | 26.7% | Kimi K2 0905 |
| Terminal-Bench Hard (Artificial Analysis run) | 23.5% | Not measured | Only Kimi K2 0905 measured |
| WeirdML | 36.7% | Not measured | Only Kimi K2 0905 measured |
| ALE-Bench | 267.1 | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Kimi K2 0905 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 15.3 | 7.6 | Kimi K2 0905 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Kimi K2 0905 | Mistral Large 2407 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 73.4% | 33.0% | Kimi K2 0905 |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Kimi K2 0905 | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 76.7% | 47.2% | Kimi K2 0905 |
| Humanity's Last Exam (Artificial Analysis run) | 6.4% | 2.9% | Kimi K2 0905 |
| 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 | 52.2% | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 17 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Kimi K2 0905 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Math Index | 57.3 | 0.0 | Kimi K2 0905 |
| AIME 2025 (Artificial Analysis run) | 57.3% | 0.0% | Kimi K2 0905 |
| 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 | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Kimi K2 0905 | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 81.9% | 68.3% | Kimi K2 0905 |
| 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 index2 boards, none shared
| Benchmark | Kimi K2 0905 | Mistral Large 2407 | Leader |
|---|---|---|---|
| Design Arena: websites | 1089 | Not measured | Only Kimi K2 0905 measured |
| Design Arena: all code categories | 1098 | Not measured | Only Kimi K2 0905 measured |
| Neither model has a result on the other 7 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Kimi K2 0905 | Mistral Large 2407 | Leader |
|---|---|---|---|
| IFBench | 41.7% | 31.6% | Kimi K2 0905 |
| Long context reasoning (AA-LCR) | 53.0% | 2.0% | Kimi K2 0905 |
| DTBench | Not measured | 61.2% | Only Mistral Large 2407 measured |
| Fiction.LiveBench | 66.7% | Not measured | Only Kimi K2 0905 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 | Kimi K2 0905 | 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
Kimi K2 0905 costs less
Repository review
50K input + 3K output tokens
Kimi K2 0905 costs less
Agent loop
200K input + 10K output tokens
Kimi K2 0905 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 | MoonshotAI Kimi K2 0905 | Mistral Large 2407 |
|---|---|---|
| Maker | Moonshot AI | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | September 4, 2025 | November 19, 2024 |
| Context window | 262K | 131K |
| Input $/Mtok | $0.60 | $2.00 |
| Output $/Mtok | $2.50 | $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 Kimi K2 0905 better than Mistral Large 2407 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Kimi K2 0905 for output tokens 2.4x cheaper ($2.50 vs $6.00 per million) and a larger context window (262K against 131K tokens).
Which is cheaper, Kimi K2 0905 or Mistral Large 2407?
Kimi K2 0905 costs $0.60 per million input tokens and $2.50 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, Kimi K2 0905 or Mistral Large 2407?
Kimi K2 0905, with 262K tokens against 131K.
How many benchmarks is the Kimi K2 0905 vs Mistral Large 2407 comparison based on?
10 benchmarks have a result for both models as of October 21, 2026. Kimi K2 0905'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.