llama-4-maverick-17b-16e-instruct-maas
vertex_ai-llama_models · chat model
llama-4-maverick-17b-16e-instruct-maas is listed here as a chat model from vertex_ai-llama_models. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.
Input
$0.3500 / 1M tokens
Output
$1.1500 / 1M tokens
Cached input
N/A
Context
1.0M
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 | $0.3500 / 1M tokens |
| Output | $1.1500 / 1M tokens |
| Embedding | $0.3500 / 1M tokens |
Token limits
Context window
1.0M
Max input tokens
1.0M
Max output tokens
1.0M
Max tokens
1.0M
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 |
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-16e-instruct-maas vertex_ai-llama_models | In $0.3500 / 1M tokens Out $1.1500 / 1M tokens |
Output: code, text | Function callingTool choice | 1.0M | Current model Reference row |
| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| llama-4-maverick-17b-128e-instruct-maas vertex_ai-llama_models | In $0.3500 / 1M tokens Out $1.1500 / 1M tokens |
Output: code, text | Function callingTool choice | 1.0M | Same provider Overall 76% |
| gemini-2.5-flash deepinfra | In $0.3000 / 1M tokens Out $2.5000 / 1M tokens | audiocode
Output: text | Function callingTool choice | 1.0M | Partial I/O overlap Overall 61% |
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