Ministral 8B Instruct vs Qwen-MT Plus vs Llama-3.1-8B-Instruct
Too close to call on our weighted score (Ministral 8B Instruct 57, Llama-3.1-8B-Instruct 56, Qwen-MT Plus 12). The right pick depends on what you value most.
Mistral AIMinistral 8B Instruct
57/100- ECI—
- Price$0.15 / $0.15
- Context131K
Alibaba (Qwen)Qwen-MT Plus
12/100- ECI—
- Price$2.46 / $7.37
- Context16K
MetaLlama-3.1-8B-Instruct
56/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
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, Qwen-MT Plus 12/100), so choose by what matters most for your work: Ministral 8B Instruct 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 priceMinistral 8B InstructMinistral 8B Instruct $0.15 · Llama-3.1-8B-Instruct $0.156 · Qwen-MT Plus $3.69 per 1M tokens (3:1 blend)
- Longest contextMinistral 8B InstructMinistral 8B Instruct 131,072 · Llama-3.1-8B-Instruct 128,000 · Qwen-MT Plus 16,384 tokens
- Widest inputsSame inputsMinistral 8B Instruct: Text · Qwen-MT Plus: Text · Llama-3.1-8B-Instruct: Text
- Self-hostingMinistral 8B Instruct and Llama-3.1-8B-InstructPublishes downloadable weights (Mistral Research License)
| Measure | Weight | Ministral 8B Instruct | Qwen-MT Plus | Llama-3.1-8B-Instruct |
|---|---|---|---|---|
| Price | 50% | 89 | 23 | 88 |
| Inputs & features | 30% | 25 | 0 | 25 |
| Context window | 20% | 24 | 0 | 24 |
| Overall | 100% | 57/100 | 12/100 | 56/100 |
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 | Ministral 8B InstructMistral AI | Qwen-MT PlusAlibaba (Qwen) | Llama-3.1-8B-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 116.6 |
| ECI rank | — | — | #145 of 148 |
| GPQA DiamondGraduate-level science questions | 27.2% (best) | — | 27.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 1.7% |
| Price per million tokens | |||
| Input | $0.15 (best) | $2.46 | $0.152 |
| Output | $0.15 (best) | $7.37 | $0.167 |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 (best) | $3.69 | $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 | 131,072 tokens (best) | 16,384 tokens | 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 | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenMistral Research License | Proprietary | Open |
| API model ID | — | qwen-mt-plus | — |
| API providers | 1 | 1 | 9 (best) |
| Released | Oct 16, 2024 | Jan 2025 | Jul 23, 2024 |
| Knowledge cutoff | — | 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 8B Instruct$1.80
- Qwen-MT Plus$39.34
- Llama-3.1-8B-Instruct$1.85
Which should you choose?
Which is better: Ministral 8B Instruct, Qwen-MT Plus or Llama-3.1-8B-Instruct?
It is close. Our weighted score puts them within a point (Ministral 8B Instruct 57/100, Llama-3.1-8B-Instruct 56/100, Qwen-MT Plus 12/100), so choose by what matters most for your work: Ministral 8B Instruct 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, Ministral 8B Instruct, Qwen-MT Plus or Llama-3.1-8B-Instruct?
Ministral 8B Instruct is cheaper at $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); Qwen-MT Plus costs $2.46 input / $7.37 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Ministral 8B Instruct versus $0.156 for Llama-3.1-8B-Instruct (1× as much) and $3.69 for Qwen-MT Plus (25× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Ministral 8B Instruct has not been scored yet, Qwen-MT Plus has not been scored yet and Llama-3.1-8B-Instruct has an ECI of 116.6.
Which is better for coding?
There are no published SWE-bench Verified results for Ministral 8B Instruct, Qwen-MT Plus and Llama-3.1-8B-Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen-MT Plus 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 16,384 for Qwen-MT Plus. Maximum output per response: Ministral 8B Instruct up to 8,192, Qwen-MT Plus up to 8,192, Llama-3.1-8B-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Ministral 8B Instruct accepts text; Qwen-MT Plus accepts text; Llama-3.1-8B-Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Ministral 8B Instruct and Llama-3.1-8B-Instruct publishes its weights (Mistral Research License) and can be self-hosted; Qwen-MT Plus is proprietary.
Which is newer?
Qwen-MT Plus is the newest, released Jan 2025. Ministral 8B Instruct came out Oct 16, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Qwen-MT Plus 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.
