Ministral 14B vs MiMo-V2-Flash vs Rnj-1 Instruct
Too close to call on our weighted score (Ministral 14B 63, MiMo-V2-Flash 60, Rnj-1 Instruct 50). The right pick depends on what you value most.
Mistral AIMinistral 14B
63/100- ECI—
- Price$0.175 / $0.175
- Context262K
XiaomiMiMo-V2-Flash
60/100- ECI—
- Price$0.10 / $0.30
- Context262K
Essential AI
Rnj-1 Instruct
50/100- ECI—
- Price$0.15 / $0.15
- Context33K
Too close to call
It is close. Our weighted score puts them within 3 points (Ministral 14B 63/100, MiMo-V2-Flash 60/100, Rnj-1 Instruct 50/100), so choose by what matters most for your work: MiMo-V2-Flash 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.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceMiMo-V2-Flash and Rnj-1 InstructMiMo-V2-Flash $0.15 · Rnj-1 Instruct $0.15 · Ministral 14B $0.175 per 1M tokens (3:1 blend)
- Longest contextMinistral 14B and MiMo-V2-FlashMinistral 14B 262,144 · MiMo-V2-Flash 262,144 · Rnj-1 Instruct 32,768 tokens
- Widest inputsMinistral 14BMinistral 14B: Text, Images · MiMo-V2-Flash: Text · Rnj-1 Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Ministral 14B | MiMo-V2-Flash | Rnj-1 Instruct |
|---|---|---|---|---|
| Price | 50% | 82 | 85 | 85 |
| Inputs & features | 30% | 50 | 35 | 25 |
| Context window | 20% | 37 | 37 | 0 |
| Overall | 100% | 63/100 | 60/100 | 50/100 |
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 | Ministral 14BMistral AI | MiMo-V2-FlashXiaomi | Rnj-1 InstructEssential AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.175 | $0.10 (best) | $0.15 |
| Output | $0.175 | $0.30 | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.175 | $0.15 (best) | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 3 providers | Median of 1 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 262,144 tokens (best) | 32,768 tokens |
| Max output | 262,144 tokens (best) | 65,536 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| 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 | No |
| Availability | |||
| Weights | OpenApache-2.0 | Open | OpenApache 2.0 |
| API model ID | — | — | — |
| API providers | 2 | 4 (best) | 1 |
| Released | Dec 2, 2025 | Dec 16, 2025 | Dec 4, 2025 |
| Knowledge cutoff | — | Dec 1, 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Ministral 14B$2.10
- MiMo-V2-Flash$1.60
- Rnj-1 Instruct$1.80
Which should you choose?
Which is better: Ministral 14B, MiMo-V2-Flash or Rnj-1 Instruct?
It is close. Our weighted score puts them within 3 points (Ministral 14B 63/100, MiMo-V2-Flash 60/100, Rnj-1 Instruct 50/100), so choose by what matters most for your work: MiMo-V2-Flash 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.
Which is cheaper, Ministral 14B, MiMo-V2-Flash or Rnj-1 Instruct?
MiMo-V2-Flash is cheaper at $0.10 input / $0.30 output per million tokens (median across 3 API providers). Rnj-1 Instruct costs $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). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for MiMo-V2-Flash versus $0.15 for Rnj-1 Instruct (1× as much) and $0.175 for Ministral 14B (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Ministral 14B has not been scored yet, MiMo-V2-Flash has not been scored yet and Rnj-1 Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Ministral 14B, MiMo-V2-Flash 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.
Which has the bigger context window?
Ministral 14B and MiMo-V2-Flash have the largest context windows (262,144 and 262,144 tokens), against 32,768 for Rnj-1 Instruct. Maximum output per response: Ministral 14B up to 262,144, MiMo-V2-Flash up to 65,536, Rnj-1 Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Ministral 14B accepts text and images; MiMo-V2-Flash accepts text; Rnj-1 Instruct accepts text. Ministral 14B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0 and Apache 2.0), so you can self-host them.
Which is newer?
MiMo-V2-Flash is the newest, released Dec 16, 2025. Rnj-1 Instruct came out Dec 4, 2025; Ministral 14B came out Dec 2, 2025. Knowledge cutoff: MiMo-V2-Flash Dec 1, 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 Nov 2, 2026.
