llama-3.1-8b-instruct
novita · chat model
llama-3.1-8b-instruct is listed here as a chat model from novita. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.
Input
$0.0200 / 1M tokens
Output
$0.0500 / 1M tokens
Cached input
N/A
Context
16.4K
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 | $0.0500 / 1M tokens |
| Embedding | $0.0200 / 1M tokens |
Token limits
Context window
16.4K
Max input tokens
16.4K
Max output tokens
16.4K
Max tokens
16.4K
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
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Comparing from
| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| llama-3.1-8b-instruct novita | In $0.0200 / 1M tokens Out $0.0500 / 1M tokens | text
Output: text | System messages | 16.4K | Current model Reference row |
| Model | Cost | Input shape | Features | Context | Why it is close |
|---|---|---|---|---|---|
| llama-3-8b-instruct novita | In $0.0400 / 1M tokens Out $0.0400 / 1M tokens | text
Output: text | System messages | 8.2K | Same provider Overall 86% |
| nvidia.nemotron-nano-9b-v2 bedrock_converse | In $0.0600 / 1M tokens Out $0.2300 / 1M tokens | text
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| qwen3-8b-fp8 novita | In $0.0350 / 1M tokens Out $0.1380 / 1M tokens | text
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| minimax.minimax-m2 bedrock_converse | In $0.3000 / 1M tokens Out $1.2000 / 1M tokens | text
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Output: text | System messages | 32.0K | Same provider Overall 71% |
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Output: text | System messages | 4.1K | Text covered Overall 70% |
| Mistral-Nemo-Instruct-2407 deepinfra | In $0.0200 / 1M tokens Out $0.0400 / 1M tokens | text
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| llama-3.1-8b llamagate | In $0.0300 / 1M tokens Out $0.0500 / 1M tokens | text
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| Llama-Guard-3-8B nebius | In $0.0200 / 1M tokens Out $0.0600 / 1M tokens | text
Output: unknown | Low overlap | 128.0K | Partial I/O overlap Overall 20% Missing text |
| Meta-Llama-3.1-8B-Instruct nebius | In $0.0200 / 1M tokens Out $0.0600 / 1M tokens | text
Output: unknown | Low overlap | 128.0K | Partial I/O overlap Overall 20% Missing text |
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