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llama-3.1-8b-instant

Groq · chat model

llama-3.1-8b-instant is listed here as a chat model from Groq. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.

Input
$0.0500 / 1M tokens
Output
$0.0800 / 1M tokens
Cached input
N/A
Context
8.2K
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
$0.0500 / 1M tokens
Output
$0.0800 / 1M tokens
Embedding
$0.0500 / 1M tokens

Token limits

Context window
8.2K
Max input tokens
128.0K
Max output tokens
8.2K
Max tokens
8.2K

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

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-3.1-8b-instant
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In $0.0500 / 1M tokens
Out $0.0800 / 1M tokens
Output: unknown
Function callingTool choice
8.2K
Current model
Reference row

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

Model Cost Input shape Features Context Why it is close
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In $0.0500 / 1M tokens
Out $0.0800 / 1M tokens
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Function callingTool choice
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In $0.0300 / 1M tokens
Out $0.0600 / 1M tokens
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In $0.0500 / 1M tokens
Out $0.0800 / 1M tokens
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In $0.0800 / 1M tokens
Out $0.0900 / 1M tokens
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Function callingTool choice
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llama-3-8b
vercel_ai_gateway
In $0.0500 / 1M tokens
Out $0.0800 / 1M tokens
text
Output: text
Tool choice
8.2K
Partial I/O overlap
Overall 48%
llama-4-maverick-17b-128e-instruct-fp8
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In $0.0500 / 1M tokens
Out $0.1000 / 1M tokens
Output: unknown
Function callingTool choice
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Partial I/O overlap
Overall 45%
gemma-3-4b-it
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In $0.0400 / 1M tokens
Out $0.0800 / 1M tokens
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Output: text
Function callingTool choice
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Partial I/O overlap
Overall 44%
gemma-3-12b-it
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In $0.0500 / 1M tokens
Out $0.1000 / 1M tokens
imagetext
Output: text
Function callingTool choice
131.1K
Partial I/O overlap
Overall 44%
qwen3-235b-a22b-2507
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In $0.0710 / 1M tokens
Out $0.1000 / 1M tokens
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Output: text
Function callingTool choice
262.1K
Partial I/O overlap
Overall 41%
llama-4-scout-17b-16e-instruct
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In $0.1100 / 1M tokens
Out $0.3400 / 1M tokens
Output: unknown
Function callingTool choice
8.2K
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Overall 34%
gpt-3.5-turbo
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In $0.5000 / 1M tokens
Out $1.5000 / 1M tokens
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Function callingTool choice
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Partial I/O overlap
Overall 34%
gpt-35-turbo
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In $0.5000 / 1M tokens
Out $1.5000 / 1M tokens
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Output: text
Function callingTool choice
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Partial I/O overlap
Overall 34%
google.gemma-3-4b-it
bedrock_converse
In $0.0400 / 1M tokens
Out $0.0800 / 1M tokens
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Output: image, text
Low overlap
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Partial I/O overlap
Overall 33%
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In $3.0000 / 1M tokens
Out $4.0000 / 1M tokens
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Function callingTool choice
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In $30.0000 / 1M tokens
Out $60.0000 / 1M tokens
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Function callingTool choice
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In $30.0000 / 1M tokens
Out $60.0000 / 1M tokens
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Output: text
Function callingTool choice
4.1K
Partial I/O overlap
Overall 33%
llama-4-maverick-17b-128e-instruct
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In $0.2000 / 1M tokens
Out $0.6000 / 1M tokens
Output: unknown
Function callingTool choice
8.2K
Same provider
Overall 31%
llama-3.3-70b-versatile
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In $0.5900 / 1M tokens
Out $0.7900 / 1M tokens
Output: unknown
Function callingTool choice
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Same provider
Overall 31%
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In $0.7100 / 1M tokens
Out $0.7100 / 1M tokens
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Output: text
Function callingTool choice
2.0K
Partial I/O overlap
Overall 31%
llama-3.1-8b
vercel_ai_gateway
In $0.0500 / 1M tokens
Out $0.0800 / 1M tokens
text
Output: text
Function calling
131.1K
Partial I/O overlap
Overall 29%
kimi-k2-instruct-0905
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In $1.0000 / 1M tokens
Out $3.0000 / 1M tokens
Output: unknown
Function callingTool choice
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In $0.2000 / 1M tokens
Out $0.2000 / 1M tokens
Output: unknown
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