Mistral Nemo vs Qwen3 14B
Updated October 21, 2026. 2 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Mistral Nemo has the edge where output tokens cost 1.5x less, and where the context window is larger, 131K against 41K tokens.
- Shared results
- 2
- Mistral Nemo only
- 1
- Qwen3 14B only
- 16
- Like-for-like categories
- 2 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
No call
Only Qwen3 14B has a AA Intelligence Index result, 6.7.
Directional only
Long documents
Listed context window
Mistral Nemo
Mistral Nemo takes 131K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
No call
Mistral Nemo $0.0002, Qwen3 14B $0.0002 per turn.
List prices
At a glance
- Coding index
- Mistral Nemo not benchmarked, Qwen3 14B not benchmarked.
- Widest category
- Intelligence, 24.4 points apart on average over 1 shared benchmark.
- Widest single gap
- Epoch Capabilities Index: Mistral Nemo 118.7 against Qwen3 14B 138.2, 24.4 points apart on a 0 to 100 scale.
- Output price
- Mistral Nemo $0.15 against Qwen3 14B $0.23 per million output tokens, 1.5x apart.
- Context window
- Mistral Nemo takes 131K tokens, Qwen3 14B 41K.
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 Nemo | Bars | Qwen3 14B | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 2 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards, none shared
| Benchmark | Mistral Nemo | Qwen3 14B | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 52.3% | Only Qwen3 14B measured |
| SciCode | Not measured | 30.7% | Only Qwen3 14B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 5.3% | Only Qwen3 14B measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Nemo | Qwen3 14B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.7 | Only Qwen3 14B measured |
| Epoch Capabilities Index | 118.7 | 138.2 | Qwen3 14B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mistral Nemo | Qwen3 14B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 34.5% | Only Qwen3 14B measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Mistral Nemo | Qwen3 14B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 60.4% | Only Qwen3 14B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.5% | Only Qwen3 14B measured |
| GPQA Diamond (Epoch AI run) | Not measured | 63.8% | Only Qwen3 14B measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Mistral Nemo | Qwen3 14B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 58.0 | Only Qwen3 14B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 58.0% | Only Qwen3 14B measured |
| OTIS Mock AIME | Not measured | 66.4% | Only Qwen3 14B 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 Nemo | Qwen3 14B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 77.4% | Only Qwen3 14B measured |
| MMLU | 65.3% | Not measured | Only Mistral Nemo measured |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Nemo | Qwen3 14B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Nemo | Qwen3 14B | Leader |
|---|---|---|---|
| IFBench | Not measured | 40.5% | Only Qwen3 14B measured |
| Long context reasoning (AA-LCR) | Not measured | 0.0% | Only Qwen3 14B measured |
| DTBench | 48.6% | 64.0% | Qwen3 14B |
| LMCA | Not measured | 18.2% | Only Qwen3 14B measured |
| Fiction.LiveBench | Not measured | 62.5% | Only Qwen3 14B measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Nemo | Qwen3 14B | 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
Qwen3 14B costs less
Agent loop
200K input + 10K output tokens
Qwen3 14B 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 Nemo | Qwen3 14B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | Open weights | Open weights |
| Released | July 19, 2024 | April 28, 2025 |
| Context window | 131K | 41K |
| Input $/Mtok | $0.15 | $0.11 |
| Output $/Mtok | $0.15 | $0.23 |
| 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 Nemo better than Qwen3 14B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Mistral Nemo has the edge where output tokens cost 1.5x less, and where the context window is larger, 131K against 41K tokens.
Which is cheaper, Mistral Nemo or Qwen3 14B?
Mistral Nemo costs $0.15 per million input tokens and $0.15 per million output tokens; Qwen3 14B costs $0.11 and $0.23. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Nemo or Qwen3 14B?
Mistral Nemo, with 131K tokens against 41K.
How many benchmarks is the Mistral Nemo vs Qwen3 14B comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. Mistral Nemo's coding index rests on 0 independent benchmarks and Qwen3 14B's on 0. Where only one model has a result, the page says so instead of naming a winner.