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llama-v3p1-405b-instruct

fireworks_ai · chat model

llama-v3p1-405b-instruct is listed here as a chat model from fireworks_ai. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.

Input
$3.0000 / 1M tokens
Output
$3.0000 / 1M tokens
Cached input
N/A
Context
16.4K
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
$3.0000 / 1M tokens
Output
$3.0000 / 1M tokens
Embedding
$3.0000 / 1M tokens

Token limits

Context window
16.4K
Max input tokens
128.0K
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 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
llama-v3p1-405b-instruct
fireworks_ai
In $3.0000 / 1M tokens
Out $3.0000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
16.4K
Current model
Reference row

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

Model Cost Input shape Features Context Why it is close
kimi-k2-instruct
fireworks_ai
In $0.6000 / 1M tokens
Out $2.5000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
16.4K
Same provider
Overall 91%
DeepSeek-R1
Together AI
In $3.0000 / 1M tokens
Out $7.0000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
20.5K
Text covered
Overall 84%
mimo-v2.5-pro
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In $1.0000 / 1M tokens
Out $3.0000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
16.4K
Text covered
Overall 84%
open-mixtral-8x22b
Mistral
In $2.0000 / 1M tokens
Out $6.0000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
8.2K
Text covered
Overall 84%
kimi-k2-instruct-0905
fireworks_ai
In $0.6000 / 1M tokens
Out $2.5000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
32.8K
Same provider
Overall 83%
command-a
vercel_ai_gateway
In $2.5000 / 1M tokens
Out $10.0000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
8.0K
Text covered
Overall 83%
mistral-large-2407
Mistral
In $3.0000 / 1M tokens
Out $9.0000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
128.0K
Text covered
Overall 80%
firefunction-v2
fireworks_ai
In $0.9000 / 1M tokens
Out $0.9000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
8.2K
Same provider
Overall 79%
mistral-large-2411
Mistral
In $2.0000 / 1M tokens
Out $6.0000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
128.0K
Text covered
Overall 78%
mixtral-8x22b-instruct-hf
fireworks_ai
In $1.2000 / 1M tokens
Out $1.2000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
65.5K
Same provider
Overall 77%
yi-large
fireworks_ai
In $3.0000 / 1M tokens
Out $3.0000 / 1M tokens
text
Output: text
Response schema
32.8K
Same provider
Overall 76%
deepseek-r1
fireworks_ai
In $3.0000 / 1M tokens
Out $8.0000 / 1M tokens
text
Output: text
Response schema
20.5K
Same provider
Overall 73%
minimax-m2p1
fireworks_ai
In $0.3000 / 1M tokens
Out $1.2000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
204.8K
Same provider
Overall 72%
minimax-m2p1
fireworks_ai
In $0.3000 / 1M tokens
Out $1.2000 / 1M tokens
text
Output: text
Function callingTool choiceResponse schema
204.8K
Same provider
Overall 72%
magistral-medium
vercel_ai_gateway
In $2.0000 / 1M tokens
Out $5.0000 / 1M tokens
text image
Output: text
Function callingTool choiceResponse schema
64.0K
Text covered
Overall 69%
kimi-k2p5
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In $0.6000 / 1M tokens
Out $3.0000 / 1M tokens
text image
Output: text
Function callingTool choiceResponse schema
262.1K
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Overall 67%
kimi-k2p5
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In $0.6000 / 1M tokens
Out $3.0000 / 1M tokens
text image
Output: text
Function callingTool choiceResponse schema
262.1K
Same provider
Overall 67%
meta.llama3-70b-instruct-v1:0
Bedrock
In $2.6500 / 1M tokens
Out $3.5000 / 1M tokens
text
Output: text
Low overlap
8.2K
Text covered
Overall 65%
meta.llama3-70b-instruct-v1:0
Bedrock
In $2.6500 / 1M tokens
Out $3.5000 / 1M tokens
text pdf
Output: text
Low overlap
2.0K
Text covered
Overall 59%
meta.llama3-70b-instruct-v1:0
Bedrock
In $2.6500 / 1M tokens
Out $3.5000 / 1M tokens
text pdf
Output: text
Low overlap
2.0K
Text covered
Overall 59%
mistral-nemo@2407
vertex_ai-mistral_models
In $3.0000 / 1M tokens
Out $3.0000 / 1M tokens
text
Output: unknown
Function callingTool choice
128.0K
Partial I/O overlap
Overall 39%
Missing text
Qwen3-235B-A22B-Instruct-2507
crusoe
In $3.0000 / 1M tokens
Out $3.0000 / 1M tokens
text
Output: unknown
Function callingTool choice
262.1K
Partial I/O overlap
Overall 31%
Missing text