LongCat 2.0 vs Mistral Large 2407
LongCat 2.0 leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 19.1 to 7.6, and LongCat 2.0 costs 5.0x less per output token ($1.20 vs $6.00 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
LongCat 2.0 or Mistral Large 2407: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick LongCat 2.0 for output tokens 5.0x cheaper ($1.20 vs $6.00 per million) and a larger context window (1.05M against 131K tokens).
- Shared results
- 4
- LongCat 2.0 only
- 4
- Mistral Large 2407 only
- 13
- Like-for-like categories
- 3 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
LongCat 2.0
LongCat 2.0 leads, 19.1 against 7.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only LongCat 2.0 has a AA Agentic Index result, 14.0.
Directional only
Long documents
Listed context window
LongCat 2.0
LongCat 2.0 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
LongCat 2.0
LongCat 2.0 $0.0009, Mistral Large 2407 $0.0050 per turn.
List prices
At a glance
- Coding index
- LongCat 2.0 not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- General, 63.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): LongCat 2.0 65.0% against Mistral Large 2407 2.0%, 63.0 points apart on a 0 to 100 scale.
- Output price
- LongCat 2.0 $1.20 against Mistral Large 2407 $6.00 per million output tokens, 5.0x apart.
- Context window
- LongCat 2.0 takes 1.05M 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 | LongCat 2.0 | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 4 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | LongCat 2.0 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Coding Index | 45.3 | Not measured | Only LongCat 2.0 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 26.7% | Only Mistral Large 2407 measured |
| SciCode | 36.3% | Not measured | Only LongCat 2.0 measured |
| ALE-Bench | 793.5 | Not measured | Only LongCat 2.0 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 | LongCat 2.0 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 19.1 | 7.6 | LongCat 2.0 |
| 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 | LongCat 2.0 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Agentic Index | 14.0 | Not measured | Only LongCat 2.0 measured |
| τ²-Bench Telecom | Not measured | 33.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | LongCat 2.0 | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 78.0% | 47.2% | LongCat 2.0 |
| Humanity's Last Exam (Artificial Analysis run) | 33.7% | 2.9% | LongCat 2.0 |
| GPQA Diamond (Epoch AI run) | Not measured | 49.0% | Only Mistral Large 2407 measured |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | LongCat 2.0 | 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 | LongCat 2.0 | 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 designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | LongCat 2.0 | Mistral Large 2407 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | LongCat 2.0 | Mistral Large 2407 | Leader |
|---|---|---|---|
| IFBench | Not measured | 31.6% | Only Mistral Large 2407 measured |
| Long context reasoning (AA-LCR) | 65.0% | 2.0% | LongCat 2.0 |
| DTBench | Not measured | 61.2% | Only Mistral Large 2407 measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| 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 | LongCat 2.0 | 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
LongCat 2.0 costs less
Repository review
50K input + 3K output tokens
LongCat 2.0 costs less
Agent loop
200K input + 10K output tokens
LongCat 2.0 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 | Meituan LongCat 2.0 | Mistral Large 2407 |
|---|---|---|
| Maker | Meituan | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | July 20, 2026 | November 19, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $0.30 | $2.00 |
| Output $/Mtok | $1.20 | $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 LongCat 2.0 better than Mistral Large 2407 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick LongCat 2.0 for output tokens 5.0x cheaper ($1.20 vs $6.00 per million) and a larger context window (1.05M against 131K tokens).
Which is cheaper, LongCat 2.0 or Mistral Large 2407?
LongCat 2.0 costs $0.30 per million input tokens and $1.20 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, LongCat 2.0 or Mistral Large 2407?
LongCat 2.0, with 1.05M tokens against 131K.
How many benchmarks is the LongCat 2.0 vs Mistral Large 2407 comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. LongCat 2.0'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.