Mistral Large 2407 vs Qwen3 VL 30B A3B Thinking
Qwen3 VL 30B A3B Thinking leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 9.5 to 7.6, and Qwen3 VL 30B A3B Thinking costs 6.0x less per output token ($1.00 vs $6.00 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
Mistral Large 2407 or Qwen3 VL 30B A3B Thinking: 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 AA Intelligence Index, where it leads.
- Shared results
- 1
- Mistral Large 2407 only
- 16
- Qwen3 VL 30B A3B Thinking only
- 0
- Like-for-like categories
- 1 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
Qwen3 VL 30B A3B Thinking
Qwen3 VL 30B A3B Thinking leads, 9.5 against 7.6.
Same board, both measured
Long documents
Listed context window
Qwen3 VL 30B A3B Thinking
Qwen3 VL 30B A3B Thinking takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 VL 30B A3B Thinking
Mistral Large 2407 $0.0050, Qwen3 VL 30B A3B Thinking $0.0008 per turn.
List prices
At a glance
- Coding index
- Mistral Large 2407 not benchmarked, Qwen3 VL 30B A3B Thinking not benchmarked.
- Widest category
- Intelligence, 1.9 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: Mistral Large 2407 7.6 against Qwen3 VL 30B A3B Thinking 9.5, 1.9 points apart on a 0 to 100 scale.
- Output price
- Mistral Large 2407 $6.00 against Qwen3 VL 30B A3B Thinking $1.00 per million output tokens, 6.0x apart.
- Context window
- Mistral Large 2407 takes 131K tokens, Qwen3 VL 30B A3B Thinking 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 30B A3B Thinking | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board, none shared
| Benchmark | Mistral Large 2407 | Qwen3 VL 30B A3B Thinking | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 26.7% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 42 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Large 2407 | Qwen3 VL 30B A3B Thinking | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | 9.5 | Qwen3 VL 30B A3B Thinking |
| 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 | Mistral Large 2407 | Qwen3 VL 30B A3B Thinking | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.0% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen3 VL 30B A3B Thinking | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 47.2% | Not measured | Only Mistral Large 2407 measured |
| Humanity's Last Exam (Artificial Analysis run) | 2.9% | Not measured | Only Mistral Large 2407 measured |
| 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 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen3 VL 30B A3B Thinking | 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 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen3 VL 30B A3B Thinking | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 68.3% | Not measured | Only Mistral Large 2407 measured |
| MMLU | 80.0% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 18 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen3 VL 30B A3B Thinking | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen3 VL 30B A3B Thinking | 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% | 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 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen3 VL 30B A3B Thinking | 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 30B A3B Thinking costs less
Repository review
50K input + 3K output tokens
Qwen3 VL 30B A3B Thinking costs less
Agent loop
200K input + 10K output tokens
Qwen3 VL 30B A3B Thinking 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 30B A3B Thinking |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | November 19, 2024 | October 6, 2025 |
| Context window | 131K | 262K |
| Input $/Mtok | $2.00 | $0.29 |
| Output $/Mtok | $6.00 | $1.00 |
| 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 Qwen3 VL 30B A3B Thinking 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 AA Intelligence Index, where it leads.
Which is cheaper, Mistral Large 2407 or Qwen3 VL 30B A3B Thinking?
Mistral Large 2407 costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen3 VL 30B A3B Thinking costs $0.29 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large 2407 or Qwen3 VL 30B A3B Thinking?
Qwen3 VL 30B A3B Thinking, with 262K tokens against 131K.
How many benchmarks is the Mistral Large 2407 vs Qwen3 VL 30B A3B Thinking comparison based on?
1 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 30B A3B Thinking's on 0. Where only one model has a result, the page says so instead of naming a winner.