Rnj-1 Instruct vs Qwen3 30B A3B vs Ministral 3 8B
Ministral 3 8B comes out ahead, 68 to 55 and 50 on our weighted score.
Essential AI
Rnj-1 Instruct
50/100- ECI—
- Price$0.15 / $0.15
- Context33K
Alibaba (Qwen)Qwen3 30B A3B
55/100- ECI136.2
- Price$0.108 / $0.475
- Context131K
- Our pick
Mistral AIMinistral 3 8B
68/100- ECI—
- Price$0.15 / $0.15
- Context262K
Ministral 3 8B is our pick
Ministral 3 8B is the better all-round choice, scoring 68/100 against Qwen3 30B A3B (55) and Rnj-1 Instruct (50). It leads on inputs & features and context window. 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 priceRnj-1 Instruct and Ministral 3 8BRnj-1 Instruct $0.15 · Ministral 3 8B $0.15 · Qwen3 30B A3B $0.20 per 1M tokens (3:1 blend)
- Longest contextMinistral 3 8BMinistral 3 8B 262,144 · Qwen3 30B A3B 131,072 · Rnj-1 Instruct 32,768 tokens
- Widest inputsMinistral 3 8BRnj-1 Instruct: Text · Qwen3 30B A3B: Text · Ministral 3 8B: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Rnj-1 Instruct | Qwen3 30B A3B | Ministral 3 8B |
|---|---|---|---|---|
| Price | 50% | 85 | 80 | 85 |
| Inputs & features | 30% | 25 | 35 | 60 |
| Context window | 20% | 0 | 25 | 37 |
| Overall | 100% | 50/100 | 55/100 | 68/100 |
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 | Rnj-1 InstructEssential AI | Qwen3 30B A3BAlibaba (Qwen) | Ministral 3 8BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 136.2 | — |
| ECI rank | — | #126 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 61.7% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 62.8% | — |
| Price per million tokens | |||
| Input | $0.15 | $0.108 (best) | $0.15 |
| Output | $0.15 (best) | $0.475 | $0.15 (best) |
| Cached input | — | — | $0.015 |
| Blended (3:1) | $0.15 (best) | $0.20 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 14 providers | Official Mistral API |
| Limits | |||
| Context window | 32,768 tokens | 131,072 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 16,384 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| 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 | Yes |
| Availability | |||
| Weights | OpenApache 2.0 | Open | OpenApache 2.0 |
| API model ID | — | — | ministral-8b-2512 |
| API providers | 1 | 14 (best) | 11 |
| Released | Dec 4, 2025 | Apr 28, 2025 | Dec 2, 2025 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Rnj-1 Instruct$1.80
- Qwen3 30B A3B$2.03
- Ministral 3 8B$1.80
Which should you choose?
Which is better: Rnj-1 Instruct, Qwen3 30B A3B or Ministral 3 8B?
Ministral 3 8B is the better all-round choice, scoring 68/100 against Qwen3 30B A3B (55) and Rnj-1 Instruct (50). It leads on inputs & features and context window. 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, Rnj-1 Instruct, Qwen3 30B A3B or Ministral 3 8B?
Rnj-1 Instruct is cheaper at $0.15 input / $0.15 output per million tokens (median across 1 API provider). Ministral 3 8B costs $0.15 input / $0.15 output per million tokens (official Mistral API price); 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 $0.15 for Ministral 3 8B (1× as much) and $0.20 for Qwen3 30B A3B (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Rnj-1 Instruct has not been scored yet, Qwen3 30B A3B has an ECI of 136.2 and Ministral 3 8B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Rnj-1 Instruct, Qwen3 30B A3B and Ministral 3 8B 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.
Which has the bigger context window?
Ministral 3 8B 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: Rnj-1 Instruct up to 32,768, Qwen3 30B A3B up to 16,384, Ministral 3 8B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Rnj-1 Instruct accepts text; Qwen3 30B A3B accepts text; Ministral 3 8B accepts text and images. Ministral 3 8B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Rnj-1 Instruct is the newest, released Dec 4, 2025. Ministral 3 8B came out Dec 2, 2025; Qwen3 30B A3B came out Apr 28, 2025.
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 Nov 2, 2026.
