Llama-4-Maverick-17B-128E-Instruct-FP8
meta_llama · chat model
Llama-4-Maverick-17B-128E-Instruct-FP8 is listed here as a chat model from meta_llama. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.
Input
N/A
Output
N/A
Cached input
N/A
Context
4.0K
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 |
|---|
Token limits
Context window
4.0K
Max input tokens
1.0M
Max output tokens
4.0K
Max tokens
4.0K
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
This list is ranked by overall similarity. Use filters to emphasize the lens that matters most for the replacement you are making.
Comparing from
| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| Llama-4-Maverick-17B-128E-Instruct-FP8 meta_llama | In N/A Out N/A |
Output: text | Function callingTool choice | 4.0K | Current model Reference row |
| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| Llama-3.3-70B-Instruct meta_llama | In N/A Out N/A |
Output: text | Function callingTool choice | 4.0K | Same provider Overall 56% |
| Llama-3.3-8B-Instruct meta_llama | In N/A Out N/A |
Output: text | Function callingTool choice | 4.0K | Same provider Overall 56% |
| Llama-4-Scout-17B-16E-Instruct-FP8 meta_llama | In N/A Out N/A |
Output: text | Function callingTool choice | 4.0K | Same provider Overall 56% |
| ministral-3b vercel_ai_gateway | In $0.0400 / 1M tokens Out $0.0400 / 1M tokens | text
Output: text | Function callingTool choice | 4.0K | Partial I/O overlap Overall 56% |
| codestral vercel_ai_gateway | In $0.3000 / 1M tokens Out $0.9000 / 1M tokens | text
Output: text | Function callingTool choice | 4.0K | Partial I/O overlap Overall 56% |
| mistral-large vercel_ai_gateway | In $2.0000 / 1M tokens Out $6.0000 / 1M tokens | imagetext
Output: text | Function callingTool choice | 4.0K | Partial I/O overlap Overall 56% |
| gpt-3.5-turbo Azure | In $0.5000 / 1M tokens Out $1.5000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Partial I/O overlap Overall 56% |
| gpt-35-turbo Azure | In $0.5000 / 1M tokens Out $1.5000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Partial I/O overlap Overall 56% |
| gpt-35-turbo-16k-0613 Azure | In $3.0000 / 1M tokens Out $4.0000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Partial I/O overlap Overall 56% |
| gpt-4 Azure | In $30.0000 / 1M tokens Out $60.0000 / 1M tokens | imagetext
Output: text | Function callingTool choice | 4.1K | Partial I/O overlap Overall 56% |
| gpt-4-0613 Azure | In $30.0000 / 1M tokens Out $60.0000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Partial I/O overlap Overall 56% |
| Llama-3.3-70B-Instruct azure_ai | In $0.7100 / 1M tokens Out $0.7100 / 1M tokens | text
Output: text | Function callingTool choice | 2.0K | Partial I/O overlap Overall 49% |
| Llama-3.2-3B-Instruct deepinfra | In $0.0200 / 1M tokens Out $0.0200 / 1M tokens | text
Output: text | Function callingTool choice | 131.1K | Partial I/O overlap Overall 41% |
| paddleocr-vl novita | In $0.0200 / 1M tokens Out $0.0200 / 1M tokens | imagetext
Output: text | Low overlap | 16.4K | Partial I/O overlap Overall 20% |
| llama3.2-3b-instruct lambda_ai | In $0.0150 / 1M tokens Out $0.0250 / 1M tokens |
Output: unknown | Function callingTool choice | 131.1K | Partial I/O overlap Overall 13% |
| llama3.2-11b-vision-instruct lambda_ai | In $0.0150 / 1M tokens Out $0.0250 / 1M tokens |
Output: unknown | Function callingTool choice | 131.1K | Partial I/O overlap Overall 10% |
| titan-embed-text-v2 vercel_ai_gateway | In $0.0200 / 1M tokens Out N/A | text
Output: embedding | Low overlap | 0 | Partial I/O overlap Overall 0% |
| Qwen2.5-Coder-3B-Instruct nscale | In $0.0100 / 1M tokens Out $0.0300 / 1M tokens |
Output: unknown | Low overlap | N/A | Partial I/O overlap Overall 0% |
No models match this filter.