Mistral Small 4 vs Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads Mistral Small 4 on the Artificial Analysis Intelligence Index, 39.9 to 11.3, and Mistral Small 4 costs 10.0x less per output token ($0.60 vs $6.00 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
Mistral Small 4 or Qwen3.8 2.4T A95B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mistral Small 4 for output tokens 10.0x cheaper ($0.60 vs $6.00 per million) and work measured by AA Coding Index or SciCode, where it leads.
- Shared results
- 7
- Mistral Small 4 only
- 8
- Qwen3.8 2.4T A95B 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
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads, 39.9 against 11.3.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads, 50.1 against 0.8.
Same board, both measured
Long documents
Listed context window
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 4
Mistral Small 4 $0.0004, Qwen3.8 2.4T A95B $0.0050 per turn.
List prices
At a glance
- Coding index
- Mistral Small 4 not benchmarked, Qwen3.8 2.4T A95B not benchmarked.
- Widest category
- Agentic work, 49.3 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Agentic Index: Mistral Small 4 0.8 against Qwen3.8 2.4T A95B 50.1, 49.3 points apart on a 0 to 100 scale.
- Output price
- Mistral Small 4 $0.60 against Qwen3.8 2.4T A95B $6.00 per million output tokens, 10.0x apart.
- Context window
- Mistral Small 4 takes 262K tokens, Qwen3.8 2.4T A95B 1.05M.
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 Small 4 | Bars | Qwen3.8 2.4T A95B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Mistral Small 4 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Coding Index | 26.6 | 71.9 | Qwen3.8 2.4T A95B |
| Vibe Code Bench (Vals AI) | 0.0% | Not measured | Only Mistral Small 4 measured |
| SciCode | 38.8% | 54.1% | Qwen3.8 2.4T A95B |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | Not measured | Only Mistral Small 4 measured |
| ALE-Bench | 497.6 | Not measured | Only Mistral Small 4 measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Small 4 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.3 | 39.9 | Qwen3.8 2.4T A95B |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Small 4 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Agentic Index | 0.8 | 50.1 | Qwen3.8 2.4T A95B |
| τ²-Bench Telecom | 41.2% | Not measured | Only Mistral Small 4 measured |
| MCP Atlas | Not measured | 84.5% | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Small 4 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 76.9% | 93.5% | Qwen3.8 2.4T A95B |
| Humanity's Last Exam (Artificial Analysis run) | 9.9% | 42.4% | Qwen3.8 2.4T A95B |
| Kagi LLM Benchmark | 40.5% | Not measured | Only Mistral Small 4 measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mistral Small 4 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| MathArena | Not measured | 31.6% | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Small 4 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Neither model has a result on 20 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Small 4 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Small 4 | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| IFBench | 48.2% | Not measured | Only Mistral Small 4 measured |
| Long context reasoning (AA-LCR) | 49.7% | 80.3% | Qwen3.8 2.4T A95B |
| DTBench | 70.9% | Not measured | Only Mistral Small 4 measured |
| LMCA | 20.6% | Not measured | Only Mistral Small 4 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 Small 4 | Qwen3.8 2.4T A95B | 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
Mistral Small 4 costs less
Repository review
50K input + 3K output tokens
Mistral Small 4 costs less
Agent loop
200K input + 10K output tokens
Mistral Small 4 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 Small 4 | Qwen3.8 2.4T A95B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | Open weights | Open weights |
| Released | March 16, 2026 | August 12, 2026 |
| Context window | 262K | 1.05M |
| Input $/Mtok | $0.15 | $2.00 |
| Output $/Mtok | $0.60 | $6.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Small 4 better than Qwen3.8 2.4T A95B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Small 4 for output tokens 10.0x cheaper ($0.60 vs $6.00 per million) and work measured by AA Coding Index or SciCode, where it leads.
Which is cheaper, Mistral Small 4 or Qwen3.8 2.4T A95B?
Mistral Small 4 costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.8 2.4T A95B costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Small 4 or Qwen3.8 2.4T A95B?
Qwen3.8 2.4T A95B, with 1.05M tokens against 262K.
How many benchmarks is the Mistral Small 4 vs Qwen3.8 2.4T A95B comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. Mistral Small 4's coding index rests on 1 independent benchmark and Qwen3.8 2.4T A95B's on 0. Where only one model has a result, the page says so instead of naming a winner.