Llama-3.1-8B-Instruct vs Ministral 8B Instruct vs Qwen2.5-Coder-0.5B
Too close to call on our weighted score (Ministral 8B Instruct 57, Llama-3.1-8B-Instruct 56, Qwen2.5-Coder-0.5B 49). The right pick depends on what you value most.
MetaLlama-3.1-8B-Instruct
56/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
Mistral AIMinistral 8B Instruct
57/100- ECI—
- Price$0.15 / $0.15
- Context131K
Alibaba (Qwen)Qwen2.5-Coder-0.5B
49/100- ECI88.2
- Price$0.10 / $0.10
- Context33K
Too close to call
It is close. Our weighted score puts them within a point (Ministral 8B Instruct 57/100, Llama-3.1-8B-Instruct 56/100, Qwen2.5-Coder-0.5B 49/100), so choose by what matters most for your work: Qwen2.5-Coder-0.5B on price and Ministral 8B Instruct for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen2.5-Coder-0.5BQwen2.5-Coder-0.5B $0.10 · Ministral 8B Instruct $0.15 · Llama-3.1-8B-Instruct $0.156 per 1M tokens (3:1 blend)
- Longest contextMinistral 8B InstructMinistral 8B Instruct 131,072 · Llama-3.1-8B-Instruct 128,000 · Qwen2.5-Coder-0.5B 32,768 tokens
- Widest inputsSame inputsLlama-3.1-8B-Instruct: Text · Ministral 8B Instruct: Text · Qwen2.5-Coder-0.5B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.1-8B-Instruct | Ministral 8B Instruct | Qwen2.5-Coder-0.5B |
|---|---|---|---|---|
| Price | 50% | 88 | 89 | 97 |
| Inputs & features | 30% | 25 | 25 | 0 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 56/100 | 57/100 | 49/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama-3.1-8B-InstructMeta | Ministral 8B InstructMistral AI | Qwen2.5-Coder-0.5BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 116.6 (best) | — | 88.2 |
| ECI rank | #145 of 148 (best) | — | #148 of 148 |
| GPQA DiamondGraduate-level science questions | 27.0% | 27.2% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 1.7% | — | — |
| Price per million tokens | |||
| Input | $0.152 | $0.15 | $0.10 (best) |
| Output | $0.167 | $0.15 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.156 | $0.15 | $0.10 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 9 providers | Median of 1 providers | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 32,768 tokens |
| Max output | 4,096 tokens | 8,192 tokens (best) | 8,192 tokens (best) |
| 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 | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenMistral Research License | OpenApache 2.0 |
| API model ID | — | — | — |
| API providers | 9 (best) | 1 | 1 |
| Released | Jul 23, 2024 | Oct 16, 2024 | Nov 12, 2024 |
| Knowledge cutoff | 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.
- Llama-3.1-8B-Instruct$1.85
- Ministral 8B Instruct$1.80
- Qwen2.5-Coder-0.5B$1.20
Which should you choose?
Which is better: Llama-3.1-8B-Instruct, Ministral 8B Instruct or Qwen2.5-Coder-0.5B?
It is close. Our weighted score puts them within a point (Ministral 8B Instruct 57/100, Llama-3.1-8B-Instruct 56/100, Qwen2.5-Coder-0.5B 49/100), so choose by what matters most for your work: Qwen2.5-Coder-0.5B on price and Ministral 8B Instruct for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Llama-3.1-8B-Instruct, Ministral 8B Instruct or Qwen2.5-Coder-0.5B?
Qwen2.5-Coder-0.5B is cheaper at $0.10 input / $0.10 output per million tokens (median across 1 API provider). Ministral 8B Instruct costs $0.15 input / $0.15 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). At a typical mix of three input tokens to one output token, that is $0.10 per million tokens for Qwen2.5-Coder-0.5B versus $0.15 for Ministral 8B Instruct (1.5× as much) and $0.156 for Llama-3.1-8B-Instruct (1.6× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama-3.1-8B-Instruct has an ECI of 116.6, Ministral 8B Instruct has not been scored yet and Qwen2.5-Coder-0.5B has an ECI of 88.2.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.1-8B-Instruct, Ministral 8B Instruct and Qwen2.5-Coder-0.5B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen2.5-Coder-0.5B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Ministral 8B Instruct has the largest context window at 131,072 tokens, against 128,000 for Llama-3.1-8B-Instruct and 32,768 for Qwen2.5-Coder-0.5B. Maximum output per response: Llama-3.1-8B-Instruct up to 4,096, Ministral 8B Instruct up to 8,192, Qwen2.5-Coder-0.5B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.1-8B-Instruct accepts text; Ministral 8B Instruct accepts text; Qwen2.5-Coder-0.5B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Mistral Research License and Apache 2.0), so you can self-host them.
Which is newer?
Qwen2.5-Coder-0.5B is the newest, released Nov 12, 2024. Ministral 8B Instruct came out Oct 16, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: 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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