Llama 4 Scout 17B Instruct vs Rnj-1 Instruct vs Gemma 3 27B IT
Llama 4 Scout 17B Instruct comes out ahead, 72 to 62 and 50 on our weighted score, though Rnj-1 Instruct is 45% cheaper per token.
- 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
GoogleGemma 3 27B IT
62/100- ECI129.9
- Price$0.11 / $0.30
- Context131K
Llama 4 Scout 17B Instruct is our pick
Llama 4 Scout 17B Instruct is the better all-round choice, scoring 72/100 against Gemma 3 27B IT (62) and Rnj-1 Instruct (50). It leads on 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 InstructRnj-1 Instruct $0.15 · Gemma 3 27B IT $0.157 · 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 · Gemma 3 27B IT 131,072 · Rnj-1 Instruct 32,768 tokens
- Widest inputsLlama 4 Scout 17B Instruct and Gemma 3 27B ITLlama 4 Scout 17B Instruct: Text, Images · Rnj-1 Instruct: Text · Gemma 3 27B IT: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama 4 Scout 17B Instruct | Rnj-1 Instruct | Gemma 3 27B IT |
|---|---|---|---|---|
| Price | 50% | 73 | 85 | 84 |
| Inputs & features | 30% | 50 | 25 | 50 |
| Context window | 20% | 100 | 0 | 25 |
| Overall | 100% | 72/100 | 50/100 | 62/100 |
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 | Llama 4 Scout 17B InstructMeta | Rnj-1 InstructEssential AI | Gemma 3 27B ITGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 129.6 | — | 129.9 (best) |
| ECI rank | #138 of 165 | — | #137 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 51.8% (best) | — | 47.7% |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.8% | — | 22.5% (best) |
| Price per million tokens | |||
| Input | $0.17 | $0.15 | $0.11 (best) |
| Output | $0.59 | $0.15 (best) | $0.30 |
| Cached input | — | — | — |
| Blended (3:1) | $0.275 | $0.15 (best) | $0.157 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 13 providers | Median of 1 providers | Median of 16 providers |
| Limits | |||
| Context window | 10,000,000 tokens (best) | 32,768 tokens | 131,072 tokens |
| Max output | 16,384 tokens | 32,768 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenApache 2.0 | Open |
| API model ID | — | — | — |
| API providers | 13 | 1 | 17 (best) |
| Released | Apr 5, 2025 | Dec 4, 2025 | Mar 12, 2025 |
| Knowledge cutoff | Aug 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.
- Llama 4 Scout 17B Instruct$2.88
- Rnj-1 Instruct$1.80
- Gemma 3 27B IT$1.69
Which should you choose?
Which is better: Llama 4 Scout 17B Instruct, Rnj-1 Instruct or Gemma 3 27B IT?
Llama 4 Scout 17B Instruct is the better all-round choice, scoring 72/100 against Gemma 3 27B IT (62) and Rnj-1 Instruct (50). It leads on 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, Llama 4 Scout 17B Instruct, Rnj-1 Instruct or Gemma 3 27B IT?
Rnj-1 Instruct is cheaper at $0.15 input / $0.15 output per million tokens (median across 1 API provider). Gemma 3 27B IT costs $0.11 input / $0.30 output per million tokens (median across 16 API providers); 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 Rnj-1 Instruct versus $0.157 for Gemma 3 27B IT (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. Llama 4 Scout 17B Instruct has an ECI of 129.6, Rnj-1 Instruct has not been scored yet and Gemma 3 27B IT has an ECI of 129.9.
Which is better for coding?
There are no published SWE-bench Verified results for Llama 4 Scout 17B Instruct, Rnj-1 Instruct and Gemma 3 27B IT 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 131,072 for Gemma 3 27B IT and 32,768 for Rnj-1 Instruct. Maximum output per response: Llama 4 Scout 17B Instruct up to 16,384, Rnj-1 Instruct up to 32,768, Gemma 3 27B IT up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Llama 4 Scout 17B Instruct accepts text and images; Rnj-1 Instruct accepts text; Gemma 3 27B IT accepts text and images. 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?
Rnj-1 Instruct is the newest, released Dec 4, 2025. Llama 4 Scout 17B Instruct came out Apr 5, 2025; Gemma 3 27B IT came out Mar 12, 2025. Knowledge cutoff: Llama 4 Scout 17B Instruct Aug 2024, Gemma 3 27B IT 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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