Rnj-1 Instruct vs Voxtral Small 24B 2507 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
Mistral AIVoxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.40
- Context33K
- Our pickMistral AI
Ministral 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 Voxtral Small 24B 2507 (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 · Voxtral Small 24B 2507 $0.175 per 1M tokens (3:1 blend)
- Longest contextMinistral 3 8BMinistral 3 8B 262,144 · Rnj-1 Instruct 32,768 · Voxtral Small 24B 2507 32,768 tokens
- Widest inputsVoxtral Small 24B 2507 and Ministral 3 8BRnj-1 Instruct: Text · Voxtral Small 24B 2507: Text, Audio · 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 | Voxtral Small 24B 2507 | Ministral 3 8B |
|---|---|---|---|---|
| Price | 50% | 85 | 82 | 85 |
| Inputs & features | 30% | 25 | 45 | 60 |
| Context window | 20% | 0 | 0 | 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 | Voxtral Small 24B 2507Mistral AI | Ministral 3 8BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 | $0.10 (best) | $0.15 |
| Output | $0.15 (best) | $0.40 | $0.15 (best) |
| Cached input | — | — | $0.015 |
| Blended (3:1) | $0.15 (best) | $0.175 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Mistral API | Official Mistral API |
| Limits | |||
| Context window | 32,768 tokens | 32,768 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 32,768 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | OpenApache 2.0 | OpenApache 2.0 | OpenApache 2.0 |
| API model ID | — | voxtral-small-latest | ministral-8b-2512 |
| API providers | 1 | 10 | 11 (best) |
| Released | Dec 4, 2025 | Jul 15, 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
- Voxtral Small 24B 2507$1.80
- Ministral 3 8B$1.80
Which should you choose?
Which is better: Rnj-1 Instruct, Voxtral Small 24B 2507 or Ministral 3 8B?
Ministral 3 8B is the better all-round choice, scoring 68/100 against Voxtral Small 24B 2507 (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, Voxtral Small 24B 2507 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); Voxtral Small 24B 2507 costs $0.10 input / $0.40 output per million tokens (official Mistral API price). 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 $0.175 for Voxtral Small 24B 2507 (1.2× 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, Voxtral Small 24B 2507 has not been scored yet 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, Voxtral Small 24B 2507 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 32,768 for Rnj-1 Instruct and 32,768 for Voxtral Small 24B 2507. Maximum output per response: Rnj-1 Instruct up to 32,768, Voxtral Small 24B 2507 up to 32,768, Ministral 3 8B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Rnj-1 Instruct accepts text; Voxtral Small 24B 2507 accepts text and audio; Ministral 3 8B accepts text and images. Voxtral Small 24B 2507 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; Voxtral Small 24B 2507 came out Jul 15, 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.
