Ministral 3B vs Qwen2.5 7B Instruct vs Llama-3.1-8B-Instruct
Ministral 3B comes out ahead, 49 to 46 and 44 on our weighted score, and it is the cheaper option too.
- Our pick
Mistral AIMinistral 3B
49/100- ECI118.1
- Price$0.10 / $0.10
- Context128K
Alibaba (Qwen)Qwen2.5 7B Instruct
44/100- ECI118.5
- Price$0.175 / $0.70
- Context131K
MetaLlama-3.1-8B-Instruct
46/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
Ministral 3B is our pick
Ministral 3B is the better all-round choice, scoring 49/100 against Llama-3.1-8B-Instruct (46) and Qwen2.5 7B Instruct (44). It leads on price. 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 · Llama-3.1-8B-Instruct 116.6
- Lowest priceMinistral 3BMinistral 3B $0.10 · Llama-3.1-8B-Instruct $0.156 · Qwen2.5 7B Instruct $0.306 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Ministral 3B 128,000 · Llama-3.1-8B-Instruct 128,000 tokens
- Widest inputsSame inputsMinistral 3B: Text · Qwen2.5 7B Instruct: Text · Llama-3.1-8B-Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Ministral 3B | Qwen2.5 7B Instruct | Llama-3.1-8B-Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 38 | 38 | 36 |
| Price | 25% | 97 | 74 | 88 |
| Inputs & features | 15% | 25 | 25 | 25 |
| Context window | 10% | 24 | 24 | 24 |
| Overall | 100% | 49/100 | 44/100 | 46/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Ministral 3BMistral AI | Qwen2.5 7B InstructAlibaba (Qwen) | Llama-3.1-8B-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 118.1 | 118.5 (best) | 116.6 |
| ECI rank | #144 of 148 | #141 of 148 (best) | #145 of 148 |
| GPQA DiamondGraduate-level science questions | 25.3% | 35.5% (best) | 27.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 2.5% (best) | 1.7% |
| Price per million tokens | |||
| Input | $0.10 (best) | $0.175 | $0.152 |
| Output | $0.10 (best) | $0.70 | $0.167 |
| Cached input | — | — | — |
| Blended (3:1) | $0.10 (best) | $0.306 | $0.156 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Alibaba API | Median of 9 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 128,000 tokens |
| Max output | 8,192 tokens (best) | 8,192 tokens (best) | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | 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 | 1 | 1 | 9 (best) |
| Released | Oct 16, 2024 | Sep 19, 2024 | Jul 23, 2024 |
| Knowledge cutoff | Mar 2024 | Apr 2024 | Dec 2023 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Ministral 3B$1.20
- Qwen2.5 7B Instruct$3.15
- Llama-3.1-8B-Instruct$1.85
Which should you choose?
Which is better: Ministral 3B, Qwen2.5 7B Instruct or Llama-3.1-8B-Instruct?
Ministral 3B is the better all-round choice, scoring 49/100 against Llama-3.1-8B-Instruct (46) and Qwen2.5 7B Instruct (44). It leads on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Ministral 3B, Qwen2.5 7B Instruct or Llama-3.1-8B-Instruct?
Ministral 3B is cheaper at $0.10 input / $0.10 output per million tokens (median across 1 API provider). Llama-3.1-8B-Instruct costs $0.152 input / $0.167 output per million tokens (median across 9 API providers); 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 $0.10 per million tokens for Ministral 3B versus $0.156 for Llama-3.1-8B-Instruct (1.6× as much) and $0.306 for Qwen2.5 7B Instruct (3.1× 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 Llama-3.1-8B-Instruct 116.6 (#145 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%, Llama-3.1-8B-Instruct 27.0%, Ministral 3B 25.3%.
Which is better for coding?
There are no published SWE-bench Verified results for Ministral 3B, Qwen2.5 7B Instruct and Llama-3.1-8B-Instruct 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?
Qwen2.5 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for Ministral 3B and 128,000 for Llama-3.1-8B-Instruct. Maximum output per response: Ministral 3B up to 8,192, Qwen2.5 7B Instruct up to 8,192, Llama-3.1-8B-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Ministral 3B accepts text; Qwen2.5 7B Instruct accepts text; Llama-3.1-8B-Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Ministral 3B is the newest, released Oct 16, 2024. Qwen2.5 7B Instruct came out Sep 19, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Ministral 3B Mar 2024, Qwen2.5 7B Instruct Apr 2024, Llama-3.1-8B-Instruct Dec 2023.
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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