Mistral Large 2407 vs Mixtral 8x22B Instruct
Mistral Large 2407 leads Mixtral 8x22B Instruct on the Artificial Analysis Intelligence Index, 7.6 to 5.7, at about the same output price ($6.00 vs $6.00 per million). Updated October 22, 2026; 6 benchmarks have a result for both.
Mistral Large 2407 or Mixtral 8x22B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mistral Large 2407 for a larger context window (131K against 66K tokens) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 6
- Mistral Large 2407 only
- 11
- Mixtral 8x22B Instruct only
- 2
- Like-for-like categories
- 5 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
Mistral Large 2407
Mistral Large 2407 leads, 7.6 against 5.7.
Same board, both measured
Long documents
Listed context window
Mistral Large 2407
Mistral Large 2407 takes 131K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
No call
Mistral Large 2407 $0.0050, Mixtral 8x22B Instruct $0.0050 per turn.
List prices
At a glance
- Coding index
- Mistral Large 2407 not benchmarked, Mixtral 8x22B Instruct not benchmarked.
- Widest category
- Knowledge, 14.6 points apart on average over 1 shared benchmark.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Mistral Large 2407 68.3% against Mixtral 8x22B Instruct 53.7%, 14.6 points apart on a 0 to 100 scale.
- Output price
- Both cost $6.00 per million output tokens.
- Context window
- Mistral Large 2407 takes 131K tokens, Mixtral 8x22B Instruct 66K.
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 | Mistral Large 2407 | Bars | Mixtral 8x22B Instruct | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 1 | ||
| Knowledge | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 5 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Mistral Large 2407 | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 26.7% | 14.8% | Mistral Large 2407 |
| SciCode | Not measured | 0.0% | Only Mixtral 8x22B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mistral Large 2407 | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | 5.7 | Mistral Large 2407 |
| Epoch Capabilities Index | Not measured | 122.0 | Only Mixtral 8x22B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mistral Large 2407 | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.0% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Mistral Large 2407 | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 47.2% | 33.2% | Mistral Large 2407 |
| Humanity's Last Exam (Artificial Analysis run) | 2.9% | 4.0% | Mixtral 8x22B Instruct |
| GPQA Diamond (Epoch AI run) | 49.0% | Not measured | Only Mistral Large 2407 measured |
| SimpleBench | 22.5% | Not measured | 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 | Mistral Large 2407 | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 0.0 | Not measured | Only Mistral Large 2407 measured |
| AIME 2025 (Artificial Analysis run) | 0.0% | Not measured | Only Mistral Large 2407 measured |
| OTIS Mock AIME | 8.5% | Not measured | Only Mistral Large 2407 measured |
| MATH level 5 | 44.8% | Not measured | 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 | Mistral Large 2407 | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 68.3% | 53.7% | Mistral Large 2407 |
| MMLU | 80.0% | Not measured | 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 | Mistral Large 2407 | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mistral Large 2407 | Mixtral 8x22B Instruct | Leader |
|---|---|---|---|
| IFBench | 31.6% | Not measured | Only Mistral Large 2407 measured |
| Long context reasoning (AA-LCR) | 2.0% | Not measured | Only Mistral Large 2407 measured |
| DTBench | 61.2% | 55.1% | Mistral Large 2407 |
| Creative writing (Lech Mazur) | 69.0% | Not measured | 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 | Mistral Large 2407 | Mixtral 8x22B Instruct | 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
Same cost
Repository review
50K input + 3K output tokens
Same cost
Agent loop
200K input + 10K output tokens
Same cost
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 | Mistral Large 2407 | Mistral Mixtral 8x22B Instruct |
|---|---|---|
| Maker | Mistral AI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | November 19, 2024 | April 17, 2024 |
| Context window | 131K | 66K |
| Input $/Mtok | $2.00 | $2.00 |
| Output $/Mtok | $6.00 | $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 Mistral Large 2407 better than Mixtral 8x22B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Large 2407 for a larger context window (131K against 66K tokens) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, Mistral Large 2407 or Mixtral 8x22B Instruct?
Mistral Large 2407 costs $2.00 per million input tokens and $6.00 per million output tokens; Mixtral 8x22B Instruct costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large 2407 or Mixtral 8x22B Instruct?
Mistral Large 2407, with 131K tokens against 66K.
How many benchmarks is the Mistral Large 2407 vs Mixtral 8x22B Instruct comparison based on?
6 benchmarks have a result for both models as of October 22, 2026. Mistral Large 2407's coding index rests on 0 independent benchmarks and Mixtral 8x22B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.