Mistral Small 4 vs Qwen3 Coder Next
Mistral Small 4 leads Qwen3 Coder Next on the Artificial Analysis Intelligence Index, 11.3 to 9.2, and Mistral Small 4 costs 1.3x less per output token ($0.60 vs $0.80 per million). Updated October 21, 2026; 10 benchmarks have a result for both.
Mistral Small 4 or Qwen3 Coder Next: 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 1.3x cheaper ($0.60 vs $0.80 per million) and work measured by AA Coding Index or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 10
- Mistral Small 4 only
- 5
- Qwen3 Coder Next only
- 1
- 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 Small 4
Mistral Small 4 leads, 11.3 against 9.2.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3 Coder Next
Qwen3 Coder Next leads, 0.9 against 0.8.
Same board, both measured
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 4
Mistral Small 4 $0.0004, Qwen3 Coder Next $0.0005 per turn.
List prices
At a glance
- Coding index
- Mistral Small 4 not benchmarked, Qwen3 Coder Next not benchmarked.
- Widest category
- Agentic work, 19.2 points apart on average over 2 shared benchmarks.
- Widest single gap
- τ²-Bench Telecom: Mistral Small 4 41.2% against Qwen3 Coder Next 79.5%, 38.3 points apart on a 0 to 100 scale.
- Output price
- Mistral Small 4 $0.60 against Qwen3 Coder Next $0.80 per million output tokens, 1.3x apart.
- Context window
- Both take 262K tokens.
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 Coder Next | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 1 | 1 | ||
| General | 2 | 0 | ||
| All shared boards | 5 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | Mistral Small 4 | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Coding Index | 26.6 | 36.2 | Qwen3 Coder Next |
| Vibe Code Bench (Vals AI) | 0.0% | Not measured | Only Mistral Small 4 measured |
| SciCode | 38.8% | 36.2% | Mistral Small 4 |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | 18.2% | Qwen3 Coder Next |
| WeirdML | Not measured | 34.4% | Only Qwen3 Coder Next measured |
| ALE-Bench | 497.6 | Not measured | Only Mistral Small 4 measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mistral Small 4 | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.3 | 9.2 | Mistral Small 4 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Small 4 | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Agentic Index | 0.8 | 0.9 | Qwen3 Coder Next |
| τ²-Bench Telecom | 41.2% | 79.5% | Qwen3 Coder Next |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Mistral Small 4 | Qwen3 Coder Next | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 76.9% | 73.7% | Mistral Small 4 |
| Humanity's Last Exam (Artificial Analysis run) | 9.9% | 10.1% | Qwen3 Coder Next |
| Kagi LLM Benchmark | 40.5% | Not measured | Only Mistral Small 4 measured |
| Neither model has a result on the other 19 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Small 4 | Qwen3 Coder Next | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Small 4 | Qwen3 Coder Next | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Small 4 | Qwen3 Coder Next | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Mistral Small 4 | Qwen3 Coder Next | Leader |
|---|---|---|---|
| IFBench | 48.2% | 35.2% | Mistral Small 4 |
| Long context reasoning (AA-LCR) | 49.7% | 47.0% | Mistral Small 4 |
| 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 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Small 4 | Qwen3 Coder Next | 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
Qwen3 Coder Next costs less
Agent loop
200K input + 10K output tokens
Qwen3 Coder Next 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 Coder Next |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | Open weights | Open weights |
| Released | March 16, 2026 | February 4, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.15 | $0.12 |
| Output $/Mtok | $0.60 | $0.80 |
| 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 Coder Next for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Small 4 for output tokens 1.3x cheaper ($0.60 vs $0.80 per million) and work measured by AA Coding Index or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Mistral Small 4 or Qwen3 Coder Next?
Mistral Small 4 costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen3 Coder Next costs $0.12 and $0.80. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Small 4 or Qwen3 Coder Next?
Neither: both take 262K tokens.
How many benchmarks is the Mistral Small 4 vs Qwen3 Coder Next comparison based on?
10 benchmarks have a result for both models as of October 21, 2026. Mistral Small 4's coding index rests on 1 independent benchmark and Qwen3 Coder Next's on 0. Where only one model has a result, the page says so instead of naming a winner.