Gemma 3 4B vs Qwen2.5 7B Instruct
Gemma 3 4B costs 2.7x less per output token than Qwen2.5 7B Instruct ($0.075 vs $0.20 per million). Updated October 22, 2026; 5 benchmarks have a result for both.
Gemma 3 4B or Qwen2.5 7B Instruct: 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 2.7x cheaper ($0.075 vs $0.20 per million) and a larger context window (131K against 33K tokens).
- Shared results
- 5
- Gemma 3 4B only
- 37
- Qwen2.5 7B Instruct only
- 2
- Like-for-like categories
- 4 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
No call
Only Gemma 3 4B has a AA Intelligence Index result, 4.8.
Directional only
Long documents
Listed context window
Gemma 3 4B
Gemma 3 4B takes 131K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemma 3 4B
Gemma 3 4B $0.0001, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- Gemma 3 4B not benchmarked, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- Reasoning, 12.2 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Epoch AI run): Gemma 3 4B 23.2% against Qwen2.5 7B Instruct 35.5%, 12.2 points apart on a 0 to 100 scale.
- Output price
- Gemma 3 4B $0.075 against Qwen2.5 7B Instruct $0.20 per million output tokens, 2.7x apart.
- Context window
- Gemma 3 4B takes 131K tokens, Qwen2.5 7B Instruct 33K.
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 | Qwen2.5 7B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Math | 1 | 0 | ||
| General | 1 | 1 | ||
| All shared boards | 2 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards, none shared
| Benchmark | Gemma 3 4B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | 2.7 | Not measured | Only Gemma 3 4B measured |
| LiveCodeBench (Artificial Analysis run) | 11.2% | Not measured | Only Gemma 3 4B measured |
| 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 | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 4.8 | Not measured | Only Gemma 3 4B measured |
| Epoch Capabilities Index | 115.9 | 118.5 | Qwen2.5 7B Instruct |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Gemma 3 4B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 5.0% | Not measured | Only Gemma 3 4B measured |
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 19.6% | Not measured | Only Gemma 3 4B measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 3 4B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 29.1% | Not measured | Only Gemma 3 4B measured |
| Humanity's Last Exam (Artificial Analysis run) | 5.3% | Not measured | Only Gemma 3 4B measured |
| GPQA Diamond (Epoch AI run) | 23.2% | 35.5% | Qwen2.5 7B Instruct |
| 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 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemma 3 4B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 12.7 | Not measured | Only Gemma 3 4B measured |
| AIME 2025 (Artificial Analysis run) | 12.7% | Not measured | Only Gemma 3 4B measured |
| OTIS Mock AIME | 7.5% | 2.5% | Gemma 3 4B |
| 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 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemma 3 4B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 41.7% | Not measured | Only Gemma 3 4B measured |
| MMLU | Not measured | 72.9% | Only Qwen2.5 7B Instruct 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 | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Gemma 3 4B | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| IFBench | 28.3% | Not measured | Only Gemma 3 4B measured |
| Long context reasoning (AA-LCR) | 6.7% | Not measured | Only Gemma 3 4B measured |
| DTBench | 50.9% | 47.7% | Gemma 3 4B |
| LMCA | 2.8% | 6.4% | Qwen2.5 7B Instruct |
| 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 31 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 3 4B | Qwen2.5 7B Instruct | 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 | Qwen2.5 7B Instruct |
|---|---|---|
| Maker | Qwen | |
| Weights | Open weights | Open weights |
| Released | March 13, 2025 | October 16, 2024 |
| Context window | 131K | 33K |
| Input $/Mtok | $0.045 | $0.10 |
| Output $/Mtok | $0.075 | $0.20 |
| 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 Qwen2.5 7B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 3 4B for output tokens 2.7x cheaper ($0.075 vs $0.20 per million) and a larger context window (131K against 33K tokens).
Which is cheaper, Gemma 3 4B or Qwen2.5 7B Instruct?
Gemma 3 4B costs $0.045 per million input tokens and $0.075 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 3 4B or Qwen2.5 7B Instruct?
Gemma 3 4B, with 131K tokens against 33K.
How many benchmarks is the Gemma 3 4B vs Qwen2.5 7B Instruct comparison based on?
5 benchmarks have a result for both models as of October 22, 2026. Gemma 3 4B's coding index rests on 0 independent benchmarks and Qwen2.5 7B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.