Mistral Large 2407 vs Qwen3 VL 8B Instruct
Mistral Large 2407 leads Qwen3 VL 8B Instruct on the Artificial Analysis Intelligence Index, 7.6 to 7.3, and Qwen3 VL 8B Instruct costs 8.0x less per output token ($0.75 vs $6.00 per million). Updated October 22, 2026; 10 benchmarks have a result for both.
Mistral Large 2407 or Qwen3 VL 8B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mistral Large 2407 for work measured by LiveCodeBench (Artificial Analysis run) or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 10
- Mistral Large 2407 only
- 7
- Qwen3 VL 8B Instruct only
- 1
- 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
Mistral Large 2407
Mistral Large 2407 leads, 7.6 against 7.3.
Same board, both measured
Long documents
Listed context window
Qwen3 VL 8B Instruct
Qwen3 VL 8B Instruct takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 VL 8B Instruct
Mistral Large 2407 $0.0050, Qwen3 VL 8B Instruct $0.0006 per turn.
List prices
At a glance
- Coding index
- Mistral Large 2407 not benchmarked, Qwen3 VL 8B Instruct not benchmarked.
- Widest category
- Math, 30.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): Mistral Large 2407 2.0% against Qwen3 VL 8B Instruct 33.3%, 31.3 points apart on a 0 to 100 scale.
- Output price
- Mistral Large 2407 $6.00 against Qwen3 VL 8B Instruct $0.75 per million output tokens, 8.0x apart.
- Context window
- Mistral Large 2407 takes 131K tokens, Qwen3 VL 8B Instruct 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 | Mistral Large 2407 | Bars | Qwen3 VL 8B Instruct | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 2 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Mistral Large 2407 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 26.7% | 35.3% | Qwen3 VL 8B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 3.8% | Only Qwen3 VL 8B 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 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | 7.3 | Mistral Large 2407 |
| 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 | Mistral Large 2407 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.0% | 29.2% | Mistral Large 2407 |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large 2407 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 47.2% | 57.9% | Qwen3 VL 8B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 2.9% | 3.8% | Qwen3 VL 8B 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 | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large 2407 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 0.0 | 30.7 | Qwen3 VL 8B Instruct |
| AIME 2025 (Artificial Analysis run) | 0.0% | 30.7% | Qwen3 VL 8B Instruct |
| 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 index01
| Benchmark | Mistral Large 2407 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 68.3% | 74.9% | Qwen3 VL 8B Instruct |
| 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 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large 2407 | Qwen3 VL 8B Instruct | Leader |
|---|---|---|---|
| IFBench | 31.6% | 39.9% | Qwen3 VL 8B Instruct |
| Long context reasoning (AA-LCR) | 2.0% | 33.3% | Qwen3 VL 8B Instruct |
| DTBench | 61.2% | Not measured | Only Mistral Large 2407 measured |
| 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 | Qwen3 VL 8B 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
Qwen3 VL 8B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen3 VL 8B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen3 VL 8B Instruct 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 | Mistral Large 2407 | Qwen3 VL 8B Instruct |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | November 19, 2024 | October 14, 2025 |
| Context window | 131K | 262K |
| Input $/Mtok | $2.00 | $0.25 |
| Output $/Mtok | $6.00 | $0.75 |
| 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 Qwen3 VL 8B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Large 2407 for work measured by LiveCodeBench (Artificial Analysis run) or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, Mistral Large 2407 or Qwen3 VL 8B Instruct?
Mistral Large 2407 costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen3 VL 8B Instruct costs $0.25 and $0.75. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large 2407 or Qwen3 VL 8B Instruct?
Qwen3 VL 8B Instruct, with 262K tokens against 131K.
How many benchmarks is the Mistral Large 2407 vs Qwen3 VL 8B Instruct comparison based on?
10 benchmarks have a result for both models as of October 22, 2026. Mistral Large 2407's coding index rests on 0 independent benchmarks and Qwen3 VL 8B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.