Ministral 14B vs Qwen3 30B A3B vs Rnj-1 Instruct

Ministral 14B is the better all-round choice, scoring 63/100 against Qwen3 30B A3B (55) and Rnj-1 Instruct (50). It leads on inputs & features and context window. Rnj-1 Instruct wins on price. 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.

Rnj-1 Instruct is cheaper at $0.15 input / $0.15 output per million tokens (median across 1 API provider). Ministral 14B costs $0.175 input / $0.175 output per million tokens (median across 2 API providers); Qwen3 30B A3B costs $0.108 input / $0.475 output per million tokens (median across 14 API providers). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Rnj-1 Instruct versus $0.175 for Ministral 14B (1.2× as much) and $0.20 for Qwen3 30B A3B (1.3× as much).

There is no independent benchmark that covers all three models yet. Ministral 14B has not been scored yet, Qwen3 30B A3B has an ECI of 136.2 and Rnj-1 Instruct has not been scored yet.

There are no published SWE-bench Verified results for Ministral 14B, Qwen3 30B A3B and Rnj-1 Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.

Ministral 14B has the largest context window at 262,144 tokens, against 131,072 for Qwen3 30B A3B and 32,768 for Rnj-1 Instruct. Maximum output per response: Ministral 14B up to 262,144, Qwen3 30B A3B up to 16,384, Rnj-1 Instruct up to 32,768 tokens.

Ministral 14B accepts text and images; Qwen3 30B A3B accepts text; Rnj-1 Instruct accepts text. Ministral 14B handles the widest range of inputs.

Yes, all three publish their weights (Apache-2.0 and Apache 2.0), so you can self-host them.

Rnj-1 Instruct is the newest, released Dec 4, 2025. Ministral 14B came out Dec 2, 2025; Qwen3 30B A3B came out Apr 28, 2025.

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 Nov 2, 2026.