Llama 4 Scout 17B Instruct vs Llama 4 Maverick 17B Instruct
Llama 4 Scout 17B Instruct comes out ahead, 62 to 58 on our weighted score, and it is the cheaper option too.
- Our pick
MetaLlama 4 Scout 17B Instruct
62/100- ECI129.7
- Price$0.225 / $0.69
- Context10M
- Meta
Llama 4 Maverick 17B Instruct
58/100- ECI132.2
- Price$0.321 / $0.91
- Context1M
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Make it a three-way comparison.
Llama 4 Scout 17B Instruct is our pick
Llama 4 Scout 17B Instruct is the better all-round choice, scoring 62/100 against Llama 4 Maverick 17B Instruct (58). It leads on price and context window. Llama 4 Maverick 17B Instruct wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityLlama 4 Maverick 17B InstructCapabilities Index (ECI): Llama 4 Maverick 17B Instruct 132.2 · Llama 4 Scout 17B Instruct 129.7
- Lowest priceLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct $0.341 · Llama 4 Maverick 17B Instruct $0.468 per 1M tokens (3:1 blend)
- Longest contextLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct 10,000,000 · Llama 4 Maverick 17B Instruct 1,000,000 tokens
- Widest inputsSame inputsLlama 4 Scout 17B Instruct: Text, Images · Llama 4 Maverick 17B Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama 4 Scout 17B Instruct | Llama 4 Maverick 17B Instruct |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 52 | 56 |
| Price | 25% | 72 | 66 |
| Inputs & features | 15% | 50 | 50 |
| Context window | 10% | 100 | 60 |
| Overall | 100% | 62/100 | 58/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama 4 Scout 17B InstructMeta | Llama 4 Maverick 17B InstructMeta |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 129.7 | 132.2 (best) |
| ECI rank | #126 of 148 | #122 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 51.8% | 67.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.8% | 20.6% (best) |
| Price per million tokens | ||
| Input | $0.225 (best) | $0.321 |
| Output | $0.69 (best) | $0.91 |
| Cached input | — | — |
| Blended (3:1) | $0.341 (best) | $0.468 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 4 providers | Median of 6 providers |
| Limits | ||
| Context window | 10,000,000 tokens (best) | 1,000,000 tokens |
| Max output | 16,384 tokens | 16,384 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | — | — |
| API providers | 4 | 6 (best) |
| Released | Apr 5, 2025 | Apr 5, 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$3.63
- Llama 4 Maverick 17B Instruct$5.03
Which should you choose?
Which is better: Llama 4 Scout 17B Instruct or Llama 4 Maverick 17B Instruct?
Llama 4 Scout 17B Instruct is the better all-round choice, scoring 62/100 against Llama 4 Maverick 17B Instruct (58). It leads on price and context window. Llama 4 Maverick 17B Instruct wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Llama 4 Scout 17B Instruct or Llama 4 Maverick 17B Instruct?
Llama 4 Scout 17B Instruct is cheaper at $0.225 input / $0.69 output per million tokens (median across 4 API providers). Llama 4 Maverick 17B Instruct costs $0.321 input / $0.91 output per million tokens (median across 6 API providers). At a typical mix of three input tokens to one output token, that is $0.341 per million tokens for Llama 4 Scout 17B Instruct versus $0.468 for Llama 4 Maverick 17B Instruct (1.4× as much).
Which scores higher on benchmarks?
Llama 4 Maverick 17B Instruct scores higher on the Capabilities Index (ECI): Llama 4 Maverick 17B Instruct 132.2 (#122 of 148) and Llama 4 Scout 17B Instruct 129.7 (#126 of 148). The confidence ranges of the top two overlap (128.0–134.1 vs 124.8–131.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — Llama 4 Maverick 17B Instruct 67.0%, Llama 4 Scout 17B Instruct 51.8%; OTIS Mock AIME 2024–2025 — Llama 4 Maverick 17B Instruct 20.6%, Llama 4 Scout 17B Instruct 7.8%.
Which is better for coding?
There are no published SWE-bench Verified results for Llama 4 Scout 17B Instruct and Llama 4 Maverick 17B Instruct yet, so there is no like-for-like coding score. On overall capability, Llama 4 Maverick 17B Instruct leads, which tends to carry over to coding, but test on your own codebase. Both 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 1,000,000 for Llama 4 Maverick 17B Instruct. Maximum output per response: Llama 4 Scout 17B Instruct up to 16,384, Llama 4 Maverick 17B Instruct up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Llama 4 Scout 17B Instruct accepts text and images; Llama 4 Maverick 17B Instruct accepts text and images. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
Llama 4 Scout 17B Instruct is the newest, released Apr 5, 2025. Llama 4 Maverick 17B Instruct came out Apr 5, 2025. Knowledge cutoff: Llama 4 Scout 17B Instruct Aug 2024, Llama 4 Maverick 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 Oct 4, 2026.
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