Ministral 3B vs Qwen Turbo

Qwen Turbo is the better all-round choice, scoring 57/100 against Ministral 3B (43). 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.

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). 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).

Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond): Qwen Turbo 41.8% and Ministral 3B 25.3%. On individual benchmarks: GPQA Diamond — Qwen Turbo 41.8%, Ministral 3B 25.3%.

There are no published SWE-bench Verified results for Ministral 3B 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. Both support tool calling for agent workflows.

Qwen Turbo has the largest context window at 1,000,000 tokens, against 128,000 for Ministral 3B. Maximum output per response: Ministral 3B up to 8,192, Qwen Turbo up to 16,384 tokens.

Ministral 3B accepts text; Qwen Turbo accepts text. They handle the same number of input types.

Ministral 3B publishes its weights and can be self-hosted; Qwen Turbo is proprietary.

Qwen Turbo is the newest, released Nov 1, 2024. Ministral 3B came out Oct 16, 2024. Knowledge cutoff: Ministral 3B Mar 2024, Qwen Turbo Apr 2024.

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.