Ministral 3B vs Qwen Turbo vs Llama-3.1-8B-Instruct
Qwen Turbo comes out ahead, 57 to 43 and 42 on our weighted score, and it is the cheaper option too.
Mistral AIMinistral 3B
43/100- ECI118.1
- Price$0.10 / $0.10
- Context128K
- Our pick
Alibaba (Qwen)Qwen Turbo
57/100- ECI—
- Price$0.05 / $0.20
- Context1M
MetaLlama-3.1-8B-Instruct
42/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
Qwen Turbo is our pick
Qwen Turbo is the better all-round choice, scoring 57/100 against Ministral 3B (43) and Llama-3.1-8B-Instruct (42). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond), because Qwen Turbo has no Capabilities Index score yet.
- CapabilityQwen TurboShared benchmarks: Qwen Turbo 41.8% · Llama-3.1-8B-Instruct 27.0% · Ministral 3B 25.3%
- Lowest priceQwen TurboQwen Turbo $0.087 · Ministral 3B $0.10 · Llama-3.1-8B-Instruct $0.156 per 1M tokens (3:1 blend)
- Longest contextQwen TurboQwen Turbo 1,000,000 · Ministral 3B 128,000 · Llama-3.1-8B-Instruct 128,000 tokens
- Widest inputsSame inputsMinistral 3B: Text · Qwen Turbo: Text · Llama-3.1-8B-Instruct: Text
- Self-hostingMinistral 3B and Llama-3.1-8B-InstructPublishes downloadable weights
| Measure | Weight | Ministral 3B | Qwen Turbo | Llama-3.1-8B-Instruct |
|---|---|---|---|---|
| CapabilityShared benchmarks | 50% | 25 | 42 | 27 |
| Price | 25% | 97 | 100 | 88 |
| Inputs & features | 15% | 25 | 35 | 25 |
| Context window | 10% | 24 | 60 | 24 |
| Overall | 100% | 43/100 | 57/100 | 42/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Ministral 3BMistral AI | Qwen TurboAlibaba (Qwen) | Llama-3.1-8B-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 118.1 (best) | — | 116.6 |
| ECI rank | #144 of 148 (best) | — | #145 of 148 |
| GPQA DiamondGraduate-level science questions | 25.3% | 41.8% (best) | 27.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 6.1% (best) | 1.7% |
| Price per million tokens | |||
| Input | $0.10 | $0.05 (best) | $0.152 |
| Output | $0.10 (best) | $0.20 | $0.167 |
| Cached input | — | — | — |
| Blended (3:1) | $0.10 | $0.087 (best) | $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 | 1,000,000 tokens (best) | 128,000 tokens |
| Max output | 8,192 tokens | 16,384 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 | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | qwen-turbo | — |
| API providers | 1 | 3 | 9 (best) |
| Released | Oct 16, 2024 | Nov 1, 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
- Qwen Turbo$0.90
- Llama-3.1-8B-Instruct$1.85
Which should you choose?
Which is better: Ministral 3B, Qwen Turbo or Llama-3.1-8B-Instruct?
Qwen Turbo is the better all-round choice, scoring 57/100 against Ministral 3B (43) and Llama-3.1-8B-Instruct (42). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond), because Qwen Turbo has no Capabilities Index score yet.
Which is cheaper, Ministral 3B, Qwen Turbo or Llama-3.1-8B-Instruct?
Qwen Turbo is cheaper at $0.05 input / $0.20 output per million tokens (official Alibaba API price). Ministral 3B costs $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). At a typical mix of three input tokens to one output token, that is $0.087 per million tokens for Qwen Turbo versus $0.10 for Ministral 3B (1.1× as much) and $0.156 for Llama-3.1-8B-Instruct (1.8× as much).
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond): Qwen Turbo 41.8%, Llama-3.1-8B-Instruct 27.0% and Ministral 3B 25.3%. On individual benchmarks: GPQA Diamond — Qwen Turbo 41.8%, 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, Qwen Turbo and Llama-3.1-8B-Instruct yet, so there is no like-for-like coding score. On overall capability, Qwen Turbo 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?
Qwen Turbo has the largest context window at 1,000,000 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, Qwen Turbo up to 16,384, Llama-3.1-8B-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Ministral 3B accepts text; Qwen Turbo accepts text; Llama-3.1-8B-Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Ministral 3B and Llama-3.1-8B-Instruct publishes its weights and can be self-hosted; Qwen Turbo is proprietary.
Which is newer?
Qwen Turbo is the newest, released Nov 1, 2024. Ministral 3B came out Oct 16, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Ministral 3B Mar 2024, Qwen Turbo 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.
