Gemma 3 4B IT vs Qwen2.5 7B Instruct vs Ministral 3B
Gemma 3 4B IT comes out ahead, 53 to 49 and 44 on our weighted score, and it is the cheaper option too.
- Our pick
GoogleGemma 3 4B IT
53/100- ECI116.1
- Price$0.05 / $0.10
- Context131K
Alibaba (Qwen)Qwen2.5 7B Instruct
44/100- ECI118.5
- Price$0.175 / $0.70
- Context131K
Mistral AIMinistral 3B
49/100- ECI118.1
- Price$0.10 / $0.10
- Context128K
Gemma 3 4B IT is our pick
Gemma 3 4B IT is the better all-round choice, scoring 53/100 against Ministral 3B (49) and Qwen2.5 7B Instruct (44). It leads on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen2.5 7B InstructCapabilities Index (ECI): Qwen2.5 7B Instruct 118.5 · Ministral 3B 118.1 · Gemma 3 4B IT 116.1
- Lowest priceGemma 3 4B ITGemma 3 4B IT $0.063 · Ministral 3B $0.10 · Qwen2.5 7B Instruct $0.306 per 1M tokens (3:1 blend)
- Longest contextGemma 3 4B IT and Qwen2.5 7B InstructGemma 3 4B IT 131,072 · Qwen2.5 7B Instruct 131,072 · Ministral 3B 128,000 tokens
- Widest inputsGemma 3 4B ITGemma 3 4B IT: Text, Images · Qwen2.5 7B Instruct: Text · Ministral 3B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Gemma 3 4B IT | Qwen2.5 7B Instruct | Ministral 3B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 35 | 38 | 38 |
| Price | 25% | 100 | 74 | 97 |
| Inputs & features | 15% | 50 | 25 | 25 |
| Context window | 10% | 24 | 24 | 24 |
| Overall | 100% | 53/100 | 44/100 | 49/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemma 3 4B ITGoogle | Qwen2.5 7B InstructAlibaba (Qwen) | Ministral 3BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 116.1 | 118.5 (best) | 118.1 |
| ECI rank | #146 of 148 | #141 of 148 (best) | #144 of 148 |
| GPQA DiamondGraduate-level science questions | 23.2% | 35.5% (best) | 25.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.5% (best) | 2.5% | — |
| Price per million tokens | |||
| Input | $0.05 (best) | $0.175 | $0.10 |
| Output | $0.10 (best) | $0.70 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.063 (best) | $0.306 | $0.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Official Alibaba API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 131,072 tokens (best) | 128,000 tokens |
| Max output | 131,072 tokens (best) | 8,192 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | — | qwen2-5-7b-instruct | — |
| API providers | 7 (best) | 1 | 1 |
| Released | Mar 12, 2025 | Sep 19, 2024 | Oct 16, 2024 |
| Knowledge cutoff | Aug 2024 | Apr 2024 | Mar 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemma 3 4B IT$0.70
- Qwen2.5 7B Instruct$3.15
- Ministral 3B$1.20
Which should you choose?
Which is better: Gemma 3 4B IT, Qwen2.5 7B Instruct or Ministral 3B?
Gemma 3 4B IT is the better all-round choice, scoring 53/100 against Ministral 3B (49) and Qwen2.5 7B Instruct (44). It leads on price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Gemma 3 4B IT, Qwen2.5 7B Instruct or Ministral 3B?
Gemma 3 4B IT is cheaper at $0.05 input / $0.10 output per million tokens (median across 7 API providers). Ministral 3B costs $0.10 input / $0.10 output per million tokens (median across 1 API provider); Qwen2.5 7B Instruct costs $0.175 input / $0.70 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.063 per million tokens for Gemma 3 4B IT versus $0.10 for Ministral 3B (1.6× as much) and $0.306 for Qwen2.5 7B Instruct (4.9× as much).
Which scores higher on benchmarks?
Qwen2.5 7B Instruct scores higher on the Capabilities Index (ECI): Qwen2.5 7B Instruct 118.5 (#141 of 148), Ministral 3B 118.1 (#144 of 148) and Gemma 3 4B IT 116.1 (#146 of 148). The confidence ranges of the top two overlap (110.7–121.3 vs 107.4–121.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen2.5 7B Instruct 35.5%, Ministral 3B 25.3%, Gemma 3 4B IT 23.2%.
Which is better for coding?
There are no published SWE-bench Verified results for Gemma 3 4B IT, Qwen2.5 7B Instruct and Ministral 3B yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 7B Instruct leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Gemma 3 4B IT and Qwen2.5 7B Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Ministral 3B. Maximum output per response: Gemma 3 4B IT up to 131,072, Qwen2.5 7B Instruct up to 8,192, Ministral 3B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Gemma 3 4B IT accepts text and images; Qwen2.5 7B Instruct accepts text; Ministral 3B accepts text. Gemma 3 4B IT handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Gemma 3 4B IT is the newest, released Mar 12, 2025. Ministral 3B came out Oct 16, 2024; Qwen2.5 7B Instruct came out Sep 19, 2024. Knowledge cutoff: Gemma 3 4B IT Aug 2024, Qwen2.5 7B Instruct Apr 2024, Ministral 3B Mar 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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