Meta-Llama-3.1-8B-Instruct-Turbo
deepinfra · chat model
Meta-Llama-3.1-8B-Instruct-Turbo is listed here as a chat model from deepinfra. 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 | $0.0200 / 1M tokens |
| Output | $0.0300 / 1M tokens |
| Embedding | $0.0200 / 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 |
|---|---|---|---|---|---|
| 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 |
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.
| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| Meta-Llama-3.1-8B-Instruct-Turbo deepinfra | In $0.0200 / 1M tokens Out $0.0300 / 1M tokens | text
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| Meta-Llama-3.1-8B-Instruct deepinfra | In $0.0300 / 1M tokens Out $0.0500 / 1M tokens | text
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| Phi-4 azure_ai | In $0.1250 / 1M tokens Out $0.5000 / 1M tokens | text
Output: text | Function callingTool choice | 16.4K | Text covered Overall 69% |
| gpt-3.5-turbo Azure | In $0.5000 / 1M tokens Out $1.5000 / 1M tokens | text
Output: text | Function callingTool choice | 4.1K | Text covered Overall 66% |
| gpt-35-turbo Azure | In $0.5000 / 1M tokens Out $1.5000 / 1M tokens | text
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| Llama-3.3-70B-Instruct azure_ai | In $0.7100 / 1M tokens Out $0.7100 / 1M tokens | text
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| gpt-4-0613 Azure | In $30.0000 / 1M tokens Out $60.0000 / 1M tokens | text
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Output: unknown | Function callingTool choice | 131.1K | Partial I/O overlap Overall 43% Missing text |
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| DeepSeek-R1-Distill-Llama-8B nscale | In $0.0250 / 1M tokens Out $0.0250 / 1M tokens | text
Output: unknown | Low overlap | N/A | Partial I/O overlap Overall 16% Missing text |
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