llama-4-scout
vercel_ai_gateway · chat model
llama-4-scout is listed here as a chat model from vercel_ai_gateway. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.
Quick read
Best for
Use this page when you need a fast view of cost, context size, and supported features before testing the model in your own workload.
Things to verify
Always check the provider page for discounts, cache pricing, region rules, and any model limits that may not appear in public metadata.
Pricing
| Item | Price |
|---|---|
| Input | $0.1000 / 1M tokens |
| Output | $0.3000 / 1M tokens |
| Embedding | $0.1000 / 1M tokens |
Token limits
Capabilities
| Capability | Supported |
|---|---|
| Vision | ✅ |
| Function calling | ✅ |
| Parallel function calling | — |
| Tool choice | ✅ |
| Prompt caching | — |
| Reasoning | — |
| Response schema | — |
| System messages | — |
| Audio input | — |
| Audio output | — |
| Web search | — |
| PDF input | — |
| Video input | — |
Benchmarks
Most benchmark rows are attached to the base model family rather than this provider route. Open benchmark explorer
| Benchmark | Score | Metric | Scope | Checked | Source |
|---|---|---|---|---|---|
| MMMU | 73.4 | accuracy | Base model: Llama 4 (Llama 4 Maverick) | 2026-05-31 | Link |
| ChartQA | 90.0 | relaxed_accuracy | Base model: Llama 4 (Llama 4 Maverick) | 2026-05-31 | Link |
| DocVQA | 94.4 | anls | Base model: Llama 4 (Llama 4 Maverick) | 2026-05-31 | Link |
| MMLU-Pro | 80.5 | macro_avg/acc | Base model: Llama 4 (Llama 4 Maverick) | 2026-05-31 | Link |
| GPQA Diamond | 69.8 | accuracy | Base model: Llama 4 (Llama 4 Maverick) | 2026-05-31 | Link |
| Artificial Analysis Intelligence Index | 18.4 | score | Base model: llama-4 (meta-llama/llama-4-maverick) | 2026-05-31 | Link |
| Artificial Analysis Coding Index | 15.6 | score | Base model: llama-4 (meta-llama/llama-4-maverick) | 2026-05-31 | Link |
| Artificial Analysis Agentic Index | 7.2 | score | Base model: llama-4 (meta-llama/llama-4-maverick) | 2026-05-31 | Link |
Sources
| Source links | |
| Pricing data | LiteLLM model cost map |
| Synced at | 2026-05-28 |
Docs
| Official docs |
Similar models
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| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| llama-4-scout vercel_ai_gateway | In $0.1000 / 1M tokens Out $0.3000 / 1M tokens | imagetext
Output: text | VisionFunction callingTool choice | 8.2K | Current model Reference row |
| Model | Cost | Input shape | Features | Context | Why it is close |
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| llama-3.2-11b vercel_ai_gateway | In $0.1600 / 1M tokens Out $0.1600 / 1M tokens | imagetext
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Output: text | VisionFunction callingTool choice | 16.4K | Text + image covered Overall 83% |
| Llama-4-Scout-17B-16E-Instruct azure_ai | In $0.2000 / 1M tokens Out $0.7800 / 1M tokens | imagetext
Output: text | VisionFunction callingTool choice | 16.4K | Text + image covered Overall 81% |
| Llama-3.2-11B-Vision-Instruct azure_ai | In $0.3700 / 1M tokens Out $0.3700 / 1M tokens | imagetext
Output: text | VisionFunction callingTool choice | 2.0K | Text + image covered Overall 80% |
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Output: text | VisionFunction callingTool choice | 262.1K | Text + image covered Overall 77% |
| Llama-3.2-90B-Vision-Instruct azure_ai | In $2.0400 / 1M tokens Out $2.0400 / 1M tokens | imagetext
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| labs-devstral-small-2512 Mistral | In $0.1000 / 1M tokens Out $0.3000 / 1M tokens | imagetext
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