Phi-4-multimodal-instruct vs Llama 4 Scout 17B Instruct
Too close to call on our weighted score (Phi-4-multimodal-instruct 72, Llama 4 Scout 17B Instruct 72). The right pick depends on what you value most.
MicrosoftPhi-4-multimodal-instruct
72/100- ECI—
- Price$0.07 / $0.11
- Context131K
MetaLlama 4 Scout 17B Instruct
72/100- ECI129.6
- Price$0.17 / $0.59
- Context10M
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within a point (Phi-4-multimodal-instruct 72/100, Llama 4 Scout 17B Instruct 72/100), so choose by what matters most for your work: Phi-4-multimodal-instruct on price and Llama 4 Scout 17B Instruct for long inputs. 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 pricePhi-4-multimodal-instructPhi-4-multimodal-instruct $0.08 · 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 · Phi-4-multimodal-instruct 131,072 tokens
- Widest inputsPhi-4-multimodal-instructPhi-4-multimodal-instruct: Text, Images, Audio · Llama 4 Scout 17B Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Phi-4-multimodal-instruct | Llama 4 Scout 17B Instruct |
|---|---|---|---|
| Price | 50% | 98 | 73 |
| Inputs & features | 30% | 60 | 50 |
| Context window | 20% | 25 | 100 |
| Overall | 100% | 72/100 | 72/100 |
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 | Phi-4-multimodal-instructMicrosoft | Llama 4 Scout 17B InstructMeta |
|---|---|---|
| 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 | $0.07 (best) | $0.17 |
| Output | $0.11 (best) | $0.59 |
| Cached input | — | — |
| Blended (3:1) | $0.08 (best) | $0.275 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 13 providers |
| Limits | ||
| Context window | 131,072 tokens | 10,000,000 tokens (best) |
| Max output | 131,072 tokens (best) | 16,384 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | No |
| Audio | Yes | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenMIT | Open |
| API model ID | — | — |
| API providers | 1 | 13 (best) |
| Released | Feb 26, 2025 | Apr 5, 2025 |
| Knowledge cutoff | Jun 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.
- Phi-4-multimodal-instruct$0.92
- Llama 4 Scout 17B Instruct$2.88
Which should you choose?
Which is better: Phi-4-multimodal-instruct or Llama 4 Scout 17B Instruct?
It is close. Our weighted score puts them within a point (Phi-4-multimodal-instruct 72/100, Llama 4 Scout 17B Instruct 72/100), so choose by what matters most for your work: Phi-4-multimodal-instruct on price and Llama 4 Scout 17B Instruct for long inputs. 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, Phi-4-multimodal-instruct or Llama 4 Scout 17B Instruct?
Phi-4-multimodal-instruct is cheaper at $0.07 input / $0.11 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 $0.08 per million tokens for Phi-4-multimodal-instruct versus $0.275 for Llama 4 Scout 17B Instruct (3.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Phi-4-multimodal-instruct has not been scored yet and Llama 4 Scout 17B Instruct has an ECI of 129.6.
Which is better for coding?
There are no published SWE-bench Verified results for Phi-4-multimodal-instruct and Llama 4 Scout 17B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. 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 131,072 for Phi-4-multimodal-instruct. Maximum output per response: Phi-4-multimodal-instruct up to 131,072, Llama 4 Scout 17B Instruct up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Phi-4-multimodal-instruct accepts text, images and audio; Llama 4 Scout 17B Instruct accepts text and images. Phi-4-multimodal-instruct handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights (MIT), so you can self-host them.
Which is newer?
Llama 4 Scout 17B Instruct is the newest, released Apr 5, 2025. Phi-4-multimodal-instruct came out Feb 26, 2025. Knowledge cutoff: Phi-4-multimodal-instruct Jun 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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