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Meta-Llama-3.1-8B-Instruct

nebius · chat model

Meta-Llama-3.1-8B-Instruct is listed here as a chat model from nebius. 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
$0.0600 / 1M tokens
Cached input
N/A
Context
128.0K
Compare similar models Open a side-by-side table using this route and the closest match from the current catalog. Estimate workload cost Use token counts, request volume, and cache assumptions to check a realistic monthly bill. Explore benchmark family Review source-backed benchmark rows for the attached base model family. Check data sources See how pricing data is generated and what still needs provider verification.

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
$0.0600 / 1M tokens
Embedding
$0.0200 / 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

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
Meta-Llama-3.1-8B-Instruct
nebius
In $0.0200 / 1M tokens
Out $0.0600 / 1M tokens
Output: unknown
Function calling
128.0K
Current model
Reference row

Overall blends cost, modality overlap, capabilities, and context.

Model Cost Input shape Features Context Why it is close
Mistral-Nemo-Instruct-2407
nebius
In $0.0400 / 1M tokens
Out $0.1200 / 1M tokens
Output: unknown
Function calling
128.0K
Same provider
Overall 50%
Qwen2.5-32B-Instruct
nebius
In $0.0600 / 1M tokens
Out $0.2000 / 1M tokens
Output: unknown
Function calling
128.0K
Same provider
Overall 46%
Mistral-Nemo-Instruct-2407
deepinfra
In $0.0200 / 1M tokens
Out $0.0400 / 1M tokens
text
Output: text
Function calling
131.1K
Partial I/O overlap
Overall 43%
Llama-3.3-Nemotron-Super-49B-v1
nebius
In $0.1000 / 1M tokens
Out $0.4000 / 1M tokens
Output: unknown
Function calling
131.1K
Same provider
Overall 43%
Llama-3.3-70B-Instruct
nebius
In $0.1300 / 1M tokens
Out $0.4000 / 1M tokens
Output: unknown
Function calling
128.0K
Same provider
Overall 43%
Meta-Llama-3.1-70B-Instruct
nebius
In $0.1300 / 1M tokens
Out $0.4000 / 1M tokens
Output: unknown
Function calling
128.0K
Same provider
Overall 43%
Qwen2.5-72B-Instruct
nebius
In $0.1300 / 1M tokens
Out $0.4000 / 1M tokens
Output: unknown
Function calling
128.0K
Same provider
Overall 43%
Meta-Llama-3.1-8B-Instruct
deepinfra
In $0.0300 / 1M tokens
Out $0.0500 / 1M tokens
text
Output: text
Function calling
131.1K
Partial I/O overlap
Overall 42%
Qwen2.5-Coder-7B
nebius
In $0.0100 / 1M tokens
Out $0.0300 / 1M tokens
Output: unknown
Function calling
32.8K
Same provider
Overall 39%
Llama-Guard-3-8B
nebius
In $0.0200 / 1M tokens
Out $0.0600 / 1M tokens
Output: unknown
Low overlap
128.0K
Same provider
Overall 35%
Qwen2-VL-7B-Instruct
nebius
In $0.0200 / 1M tokens
Out $0.0600 / 1M tokens
Output: unknown
Low overlap
131.1K
Same provider
Overall 35%
meta.llama3-2-1b-instruct-v1:0
Bedrock
In $0.1000 / 1M tokens
Out $0.1000 / 1M tokens
text
Output: text
Function calling
4.1K
Partial I/O overlap
Overall 34%
us.meta.llama3-2-1b-instruct-v1:0
Bedrock
In $0.1000 / 1M tokens
Out $0.1000 / 1M tokens
text
Output: text
Function calling
4.1K
Partial I/O overlap
Overall 34%
Mixtral-8x22B-Instruct-v0.1
anyscale
In $0.9000 / 1M tokens
Out $0.9000 / 1M tokens
Output: unknown
Function calling
65.5K
Partial I/O overlap
Overall 34%
Mistral-7B-Instruct-v0.1
anyscale
In $0.1500 / 1M tokens
Out $0.1500 / 1M tokens
Output: unknown
Function calling
16.4K
Partial I/O overlap
Overall 33%
Mixtral-8x7B-Instruct-v0.1
anyscale
In $0.1500 / 1M tokens
Out $0.1500 / 1M tokens
Output: unknown
Function calling
16.4K
Partial I/O overlap
Overall 33%
eu.meta.llama3-2-1b-instruct-v1:0
Bedrock
In $0.1300 / 1M tokens
Out $0.1300 / 1M tokens
text
Output: text
Function calling
4.1K
Partial I/O overlap
Overall 32%
Meta-Llama-3-8B-Instruct
deepinfra
In $0.0300 / 1M tokens
Out $0.0600 / 1M tokens
text
Output: text
Function calling
8.2K
Partial I/O overlap
Overall 30%
mistral-large-2402
Azure
In $8.0000 / 1M tokens
Out $24.0000 / 1M tokens
Output: unknown
Function calling
32.0K
Partial I/O overlap
Overall 29%
mistral-large-latest
Azure
In $8.0000 / 1M tokens
Out $24.0000 / 1M tokens
Output: unknown
Function calling
32.0K
Partial I/O overlap
Overall 29%
command-r-plus
Azure
In $3.0000 / 1M tokens
Out $15.0000 / 1M tokens
text
Output: text
Function calling
4.1K
Partial I/O overlap
Overall 26%
llama-3.2-3b-instruct
novita
In $0.0300 / 1M tokens
Out $0.0500 / 1M tokens
text
Output: text
Function calling
32.0K
Partial I/O overlap
Overall 25%
llama-3.1-8b-instruct
novita
In $0.0200 / 1M tokens
Out $0.0500 / 1M tokens
text
Output: text
Low overlap
16.4K
Partial I/O overlap
Overall 20%