Llama-3.2-1B vs Ministral 8B Instruct vs Qwen Turbo
Qwen Turbo comes out ahead, 57 to 42 and 39 on our weighted score.
Meta
Llama-3.2-1B
39/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
Mistral AI
Ministral 8B Instruct
42/100- ECI—
- Price$0.15 / $0.15
- Context131K
- Our pick
Alibaba (Qwen)
Qwen Turbo
57/100- ECI—
- Price$0.05 / $0.20
- Context1M
Qwen Turbo is our pick
Qwen Turbo is the better all-round choice, scoring 57/100 against Ministral 8B Instruct (42) and Llama-3.2-1B (39). It leads on capability, 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 Ministral 8B Instruct and Qwen Turbo has no Capabilities Index score yet.
- CapabilityQwen TurboShared benchmarks: Qwen Turbo 41.8% · Ministral 8B Instruct 27.2% · Llama-3.2-1B 23.9%
- Lowest priceLlama-3.2-1BLlama-3.2-1B $0.085 · Qwen Turbo $0.087 · Ministral 8B Instruct $0.15 per 1M tokens (3:1 blend)
- Longest contextQwen TurboQwen Turbo 1,000,000 · Llama-3.2-1B 131,072 · Ministral 8B Instruct 131,072 tokens
- Widest inputsSame inputsLlama-3.2-1B: Text · Ministral 8B Instruct: Text · Qwen Turbo: Text
- Self-hostingLlama-3.2-1B and Ministral 8B InstructPublishes downloadable weights (Llama 3.2 Community License and Mistral Research License)
| Measure | Weight | Llama-3.2-1B | Ministral 8B Instruct | Qwen Turbo |
|---|---|---|---|---|
| CapabilityShared benchmarks | 50% | 24 | 27 | 42 |
| Price | 25% | 100 | 89 | 100 |
| Inputs & features | 15% | 0 | 25 | 35 |
| Context window | 10% | 24 | 24 | 60 |
| Overall | 100% | 39/100 | 42/100 | 57/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 102.0 | — | — |
| ECI rank | #147 of 148 | — | — |
| GPQA DiamondGraduate-level science questions | 23.9% | 27.2% | 41.8% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 0.6% | — | 6.1% (best) |
| Price per million tokens | |||
| Input | $0.064 | $0.15 | $0.05 (best) |
| Output | $0.15 | $0.15 (best) | $0.20 |
| Cached input | — | — | — |
| Blended (3:1) | $0.085 (best) | $0.15 | $0.087 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 1 providers | Official Alibaba API |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 1,000,000 tokens (best) |
| Max output | 8,192 tokens | 8,192 tokens | 16,384 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 | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenLlama 3.2 Community License | OpenMistral Research License | Proprietary |
| API model ID | — | — | qwen-turbo |
| API providers | 2 | 1 | 3 (best) |
| Released | Sep 25, 2024 | Oct 16, 2024 | Nov 1, 2024 |
| Knowledge cutoff | Dec 2023 | — | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
Llama-3.2-1B$0.936
Ministral 8B Instruct$1.80
Qwen Turbo$0.90
Which should you choose?
Which is better: Llama-3.2-1B, Ministral 8B Instruct or Qwen Turbo?
Qwen Turbo is the better all-round choice, scoring 57/100 against Ministral 8B Instruct (42) and Llama-3.2-1B (39). It leads on capability, 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 Ministral 8B Instruct and Qwen Turbo has no Capabilities Index score yet.
Which is cheaper, Llama-3.2-1B, Ministral 8B Instruct or Qwen Turbo?
Llama-3.2-1B is cheaper at $0.064 input / $0.15 output per million tokens (median across 2 API providers). Qwen Turbo costs $0.05 input / $0.20 output per million tokens (official Alibaba API price); Ministral 8B Instruct costs $0.15 input / $0.15 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.085 per million tokens for Llama-3.2-1B versus $0.087 for Qwen Turbo (1× as much) and $0.15 for Ministral 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%, Ministral 8B Instruct 27.2% and Llama-3.2-1B 23.9%. On individual benchmarks: GPQA Diamond — Qwen Turbo 41.8%, Ministral 8B Instruct 27.2%, Llama-3.2-1B 23.9%.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-1B, Ministral 8B Instruct and Qwen Turbo 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. Note that Llama-3.2-1B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen Turbo has the largest context window at 1,000,000 tokens, against 131,072 for Llama-3.2-1B and 131,072 for Ministral 8B Instruct. Maximum output per response: Llama-3.2-1B up to 8,192, Ministral 8B Instruct up to 8,192, Qwen Turbo up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Ministral 8B Instruct accepts text; Qwen Turbo accepts text. They handle the same number of input types.
Are any of these open source?
Llama-3.2-1B and Ministral 8B Instruct publishes its weights (Llama 3.2 Community License and Mistral Research License) and can be self-hosted; Qwen Turbo is proprietary.
Which is newer?
Qwen Turbo is the newest, released Nov 1, 2024. Ministral 8B Instruct came out Oct 16, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, Qwen Turbo Apr 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.