MiMo-V2-Flash vs Llama 4 Scout 17B Instruct vs Rnj-1 Instruct
Llama 4 Scout 17B Instruct comes out ahead, 72 to 60 and 50 on our weighted score, though MiMo-V2-Flash is 45% cheaper per token.
XiaomiMiMo-V2-Flash
60/100- ECI—
- Price$0.10 / $0.30
- Context262K
- Our pick
MetaLlama 4 Scout 17B Instruct
72/100- ECI129.6
- Price$0.17 / $0.59
- Context10M
Essential AI
Rnj-1 Instruct
50/100- ECI—
- Price$0.15 / $0.15
- Context33K
Llama 4 Scout 17B Instruct is our pick
Llama 4 Scout 17B Instruct is the better all-round choice, scoring 72/100 against MiMo-V2-Flash (60) 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 priceMiMo-V2-Flash and Rnj-1 InstructMiMo-V2-Flash $0.15 · Rnj-1 Instruct $0.15 · Llama 4 Scout 17B Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct 10,000,000 · MiMo-V2-Flash 262,144 · Rnj-1 Instruct 32,768 tokens
- Widest inputsLlama 4 Scout 17B InstructMiMo-V2-Flash: Text · Llama 4 Scout 17B Instruct: Text, Images · Rnj-1 Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | MiMo-V2-Flash | Llama 4 Scout 17B Instruct | Rnj-1 Instruct |
|---|---|---|---|---|
| Price | 50% | 85 | 73 | 85 |
| Inputs & features | 30% | 35 | 50 | 25 |
| Context window | 20% | 37 | 100 | 0 |
| Overall | 100% | 60/100 | 72/100 | 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 | MiMo-V2-FlashXiaomi | Llama 4 Scout 17B InstructMeta | Rnj-1 InstructEssential AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 129.6 | — |
| ECI rank | — | #138 of 165 | — |
| GPQA DiamondGraduate-level science questions | — | 51.8% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 7.8% | — |
| Price per million tokens | |||
| Input | $0.10 (best) | $0.17 | $0.15 |
| Output | $0.30 | $0.59 | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 (best) | $0.275 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 13 providers | Median of 1 providers |
| Limits | |||
| Context window | 262,144 tokens | 10,000,000 tokens (best) | 32,768 tokens |
| Max output | 65,536 tokens (best) | 16,384 tokens | 32,768 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | OpenApache 2.0 |
| API model ID | — | — | — |
| API providers | 4 | 13 (best) | 1 |
| Released | Dec 16, 2025 | Apr 5, 2025 | Dec 4, 2025 |
| Knowledge cutoff | Dec 1, 2024 | Aug 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiMo-V2-Flash$1.60
- Llama 4 Scout 17B Instruct$2.88
- Rnj-1 Instruct$1.80
Which should you choose?
Which is better: MiMo-V2-Flash, Llama 4 Scout 17B Instruct or Rnj-1 Instruct?
Llama 4 Scout 17B Instruct is the better all-round choice, scoring 72/100 against MiMo-V2-Flash (60) 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, MiMo-V2-Flash, Llama 4 Scout 17B Instruct 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); Llama 4 Scout 17B Instruct costs $0.17 input / $0.59 output per million tokens (median across 13 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 $0.275 for Llama 4 Scout 17B Instruct (1.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiMo-V2-Flash has not been scored yet, Llama 4 Scout 17B Instruct has an ECI of 129.6 and Rnj-1 Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MiMo-V2-Flash, Llama 4 Scout 17B Instruct 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?
Llama 4 Scout 17B Instruct has the largest context window at 10,000,000 tokens, against 262,144 for MiMo-V2-Flash and 32,768 for Rnj-1 Instruct. Maximum output per response: MiMo-V2-Flash up to 65,536, Llama 4 Scout 17B Instruct up to 16,384, Rnj-1 Instruct up to 32,768 tokens.
Which can read images, PDFs, audio or video?
MiMo-V2-Flash accepts text; Llama 4 Scout 17B Instruct accepts text and images; Rnj-1 Instruct accepts text. Llama 4 Scout 17B Instruct 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?
MiMo-V2-Flash is the newest, released Dec 16, 2025. Rnj-1 Instruct came out Dec 4, 2025; Llama 4 Scout 17B Instruct came out Apr 5, 2025. Knowledge cutoff: MiMo-V2-Flash Dec 1, 2024, Llama 4 Scout 17B Instruct Aug 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.
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