Gemma 3 4B vs Mistral Large 2407
Mistral Large 2407 leads Gemma 3 4B on the Artificial Analysis Intelligence Index, 7.6 to 4.8, and Gemma 3 4B costs 80.0x less per output token ($0.075 vs $6.00 per million). Updated October 21, 2026; 13 benchmarks have a result for both.
Gemma 3 4B or Mistral Large 2407: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Gemma 3 4B for output tokens 80.0x cheaper ($0.075 vs $6.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 13
- Gemma 3 4B only
- 29
- Mistral Large 2407 only
- 4
- 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 4.8.
Same board, both measured
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 4B
Gemma 3 4B $0.0001, Mistral Large 2407 $0.0050 per turn.
List prices
At a glance
- Coding index
- Gemma 3 4B not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- Agentic work, 28.1 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Gemma 3 4B 5.0% against Mistral Large 2407 33.0%, 28.1 points apart on a 0 to 100 scale.
- Output price
- Gemma 3 4B $0.075 against Mistral Large 2407 $6.00 per million output tokens, 80.0x apart.
- Context window
- Both take 131K 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 | Gemma 3 4B | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 2 | ||
| Math | 2 | 1 | ||
| Knowledge | 0 | 1 | ||
| General | 1 | 2 | ||
| All shared boards | 4 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Gemma 3 4B | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Coding Index | 2.7 | Not measured | Only Gemma 3 4B measured |
| LiveCodeBench (Artificial Analysis run) | 11.2% | 26.7% | Mistral Large 2407 |
| Terminal-Bench Hard (Artificial Analysis run) | 0.8% | Not measured | Only Gemma 3 4B measured |
| LMArena Text: coding | 1230 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: software and IT | 1269 | Not measured | Only Gemma 3 4B measured |
| SciCode | 0.0% | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 3 4B | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.8 | 7.6 | Mistral Large 2407 |
| Epoch Capabilities Index | 115.9 | Not measured | Only Gemma 3 4B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 3 4B | Mistral Large 2407 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 5.0% | 33.0% | Mistral Large 2407 |
| Berkeley Function Calling Leaderboard (BFCL) | 19.6% | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | Gemma 3 4B | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 29.1% | 47.2% | Mistral Large 2407 |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | 2.9% | Gemma 3 4B |
| GPQA Diamond (Epoch AI run) | 23.2% | 49.0% | Mistral Large 2407 |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 measured |
| LMArena Text: hard prompts | 1253 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: expert | 1223 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: hard prompts in English | 1253 | Not measured | Only Gemma 3 4B measured |
| Kagi LLM Benchmark | 25.2% | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | Gemma 3 4B | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Math Index | 12.7 | 0.0 | Gemma 3 4B |
| AIME 2025 (Artificial Analysis run) | 12.7% | 0.0% | Gemma 3 4B |
| OTIS Mock AIME | 7.5% | 8.5% | Mistral Large 2407 |
| MATH level 5 | Not measured | 44.8% | Only Mistral Large 2407 measured |
| LMArena Text: math | 1239 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: mathematical work | 1256 | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 3 4B | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 41.7% | 68.3% | Mistral Large 2407 |
| MMLU | Not measured | 80.0% | Only Mistral Large 2407 measured |
| LMArena Text: science | 1306 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: medicine and healthcare | 1342 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: legal and government | 1327 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: business and finance | 1311 | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 4B | Mistral Large 2407 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | Gemma 3 4B | Mistral Large 2407 | Leader |
|---|---|---|---|
| IFBench | 28.3% | 31.6% | Mistral Large 2407 |
| Long context reasoning (AA-LCR) | 6.7% | 2.0% | Gemma 3 4B |
| DTBench | 50.9% | 61.2% | Mistral Large 2407 |
| LMCA | 2.8% | Not measured | Only Gemma 3 4B measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| LMArena Text | 1291 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: instruction following | 1239 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: creative writing | 1270 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: English | 1300 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: non-English | 1273 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: German | 1281 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: Russian | 1294 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: multi-turn | 1255 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: longer queries | 1273 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: writing and language | 1288 | Not measured | Only Gemma 3 4B measured |
| LMArena Text: entertainment and media | 1233 | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 30 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 4B | Mistral Large 2407 | 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
Gemma 3 4B costs less
Repository review
50K input + 3K output tokens
Gemma 3 4B costs less
Agent loop
200K input + 10K output tokens
Gemma 3 4B 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 | Google Gemma 3 4B | Mistral Large 2407 |
|---|---|---|
| Maker | Mistral AI | |
| Weights | Open weights | No public weights listed |
| Released | March 13, 2025 | November 19, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.045 | $2.00 |
| Output $/Mtok | $0.075 | $6.00 |
| 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 Gemma 3 4B better than Mistral Large 2407 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 3 4B for output tokens 80.0x cheaper ($0.075 vs $6.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or AA Intelligence Index, where it leads.
Which is cheaper, Gemma 3 4B or Mistral Large 2407?
Gemma 3 4B costs $0.045 per million input tokens and $0.075 per million output tokens; Mistral Large 2407 costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 4B or Mistral Large 2407?
Neither: both take 131K tokens.
How many benchmarks is the Gemma 3 4B vs Mistral Large 2407 comparison based on?
13 benchmarks have a result for both models as of October 21, 2026. Gemma 3 4B's coding index rests on 0 independent benchmarks and Mistral Large 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.