databricks-meta-llama-3-1-405b-instruct
databricks · chat model
databricks-meta-llama-3-1-405b-instruct is listed here as a chat model from databricks. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.
Input
$5.0000 / 1M tokens
Output
$15.0000 / 1M tokens
Cached input
N/A
Context
128.0K
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 |
|---|---|
| Input | $5.0000 / 1M tokens |
| Output | $15.0000 / 1M tokens |
| Embedding | $5.0000 / 1M tokens |
Token limits
Context window
128.0K
Max input tokens
128.0K
Max output tokens
128.0K
Max tokens
128.0K
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 |
|---|---|---|---|---|---|
| MMLU (CoT) | 88.6 | macro_avg/acc | Base model: Llama 3.1 (Llama 3.1 405B Instruct) | 2026-05-31 | Link |
| MMLU-Pro (CoT) | 73.3 | macro_avg/acc | Base model: Llama 3.1 (Llama 3.1 405B Instruct) | 2026-05-31 | Link |
| GPQA Diamond | 49.0 | acc | Base model: Llama 3.1 (Llama 3.1 405B Instruct) | 2026-05-31 | Link |
| HumanEval | 89.0 | pass@1 | Base model: Llama 3.1 (Llama 3.1 405B Instruct) | 2026-05-31 | Link |
| MATH (CoT) | 73.8 | sympy_intersection_score | Base model: Llama 3.1 (Llama 3.1 405B Instruct) | 2026-05-31 | Link |
| MMLU | 85.2 | macro_avg/acc_char | Base model: Llama 3.1 (Llama 3.1 405B) | 2026-05-31 | Link |
| MMLU-Pro (CoT) | 61.6 | macro_avg/acc_char | Base model: Llama 3.1 (Llama 3.1 405B) | 2026-05-31 | Link |
| AGIEval English | 71.6 | average/acc_char | Base model: Llama 3.1 (Llama 3.1 405B) | 2026-05-31 | Link |
| MMLU | 87.3 | macro_avg/acc | Base model: Llama 3.1 (Llama 3.1 405B Instruct) | 2026-05-31 | Link |
| MMLU (CoT) | 88.6 | macro_avg/acc | Base model: Llama 3.1 (Llama 3.1 405B Instruct) | 2026-05-31 | Link |
| MMLU-Pro (CoT) | 73.3 | micro_avg/acc_char | Base model: Llama 3.1 (Llama 3.1 405B Instruct) | 2026-05-31 | Link |
| IFEval | 88.6 | Base model: Llama 3.1 (Llama 3.1 405B Instruct) | 2026-05-31 | Link | |
| ARC-Challenge | 96.9 | acc | Base model: Llama 3.1 (Llama 3.1 405B Instruct) | 2026-05-31 | Link |
| GPQA | 50.7 | em | Base model: Llama 3.1 (Llama 3.1 405B Instruct) | 2026-05-31 | Link |
| MMLU | 69.4% | macro_avg/acc | Base model: Llama 3.1 (Llama-3.1-8B-Instruct) | 2026-05-31 | Link |
| MMLU | 83.6% | macro_avg/acc | Base model: Llama 3.1 (Llama-3.1-70B-Instruct) | 2026-05-31 | Link |
| HumanEval | 72.6% | pass@1 | Base model: Llama 3.1 (Llama-3.1-8B-Instruct) | 2026-05-31 | Link |
| HumanEval | 80.5% | pass@1 | Base model: Llama 3.1 (Llama-3.1-70B-Instruct) | 2026-05-31 | Link |
| GSM8K (CoT) | 84.5% | em_maj1@1 | Base model: Llama 3.1 (Llama-3.1-8B-Instruct) | 2026-05-31 | Link |
| GSM8K (CoT) | 95.1% | em_maj1@1 | Base model: Llama 3.1 (Llama-3.1-70B-Instruct) | 2026-05-31 | Link |
| BFCL | 76.1% | acc | Base model: Llama 3.1 (Llama-3.1-8B-Instruct) | 2026-05-31 | Link |
| BFCL | 84.8% | acc | Base model: Llama 3.1 (Llama-3.1-70B-Instruct) | 2026-05-31 | Link |
| Artificial Analysis Intelligence Index | 12.2 | score | Base model: llama-3.1 (meta-llama/llama-3.1-70b-instruct) | 2026-05-31 | Link |
| Artificial Analysis Coding Index | 10.9 | score | Base model: llama-3.1 (meta-llama/llama-3.1-70b-instruct) | 2026-05-31 | Link |
| Artificial Analysis Agentic Index | 5.1 | score | Base model: llama-3.1 (meta-llama/llama-3.1-70b-instruct) | 2026-05-31 | Link |
Sources
| Source links | |
| Pricing data | LiteLLM model cost map |
| Synced at | 2026-05-28 |
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Comparing from
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|---|---|---|---|---|---|
| databricks-meta-llama-3-1-405b-instruct databricks | In $5.0000 / 1M tokens Out $15.0000 / 1M tokens |
Output: unknown | Tool choice | 128.0K | Current model Reference row |
| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| databricks-meta-llama-3-70b-instruct databricks | In $1.0000 / 1M tokens Out $3.0000 / 1M tokens |
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