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llama3.1-nemotron-70b-instruct-fp8

lambda_ai · chat model

llama3.1-nemotron-70b-instruct-fp8 is listed here as a chat model from lambda_ai. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.

Input
$0.1200 / 1M tokens
Output
$0.3000 / 1M tokens
Cached input
N/A
Context
131.1K
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
$0.1200 / 1M tokens
Output
$0.3000 / 1M tokens
Embedding
$0.1200 / 1M tokens

Token limits

Context window
131.1K
Max input tokens
131.1K
Max output tokens
131.1K
Max tokens
131.1K

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
MT-Bench 8.22 total Base model: Nemotron (Nemotron-4-340B-Instruct) 2026-05-31 Link
IFEval 79.9 Prompt-Strict Acc Base model: Nemotron (Nemotron-4-340B-Instruct) 2026-05-31 Link
IFEval 86.1 Instruction-Strict Acc Base model: Nemotron (Nemotron-4-340B-Instruct) 2026-05-31 Link
MMLU 78.7 0-shot Base model: Nemotron (Nemotron-4-340B-Instruct) 2026-05-31 Link
GSM8K 92.3 0-shot Base model: Nemotron (Nemotron-4-340B-Instruct) 2026-05-31 Link
HumanEval 73.2 0-shot Base model: Nemotron (Nemotron-4-340B-Instruct) 2026-05-31 Link
MBPP 75.4 0-shot Base model: Nemotron (Nemotron-4-340B-Instruct) 2026-05-31 Link
Arena Hard 54.2 Arena Hard Base model: Nemotron (Nemotron-4-340B-Instruct) 2026-05-31 Link
AlpacaEval 2.0 LC 41.5 Length Controlled Base model: Nemotron (Nemotron-4-340B-Instruct) 2026-05-31 Link
AIME 2025 76.25% Reasoning On Base model: Nemotron (NVIDIA-Nemotron-Nano-12B-v2) 2026-05-31 Link
MATH-500 97.75% Reasoning On Base model: Nemotron (NVIDIA-Nemotron-Nano-12B-v2) 2026-05-31 Link
GPQA 64.48% Reasoning On Base model: Nemotron (NVIDIA-Nemotron-Nano-12B-v2) 2026-05-31 Link
LCB 70.79% Reasoning On Base model: Nemotron (NVIDIA-Nemotron-Nano-12B-v2) 2026-05-31 Link
BFCL 66.98% Reasoning On Base model: Nemotron (NVIDIA-Nemotron-Nano-12B-v2) 2026-05-31 Link
IFEval Prompt 84.70% Reasoning On Base model: Nemotron (NVIDIA-Nemotron-Nano-12B-v2) 2026-05-31 Link
IFEval Instruction 89.81% Reasoning On Base model: Nemotron (NVIDIA-Nemotron-Nano-12B-v2) 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
llama3.1-nemotron-70b-instruct-fp8
lambda_ai
In $0.1200 / 1M tokens
Out $0.3000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
131.1K
Current model
Reference row

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

Model Cost Input shape Features Context Why it is close
Qwen2.5-72B-Instruct
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In $0.1200 / 1M tokens
Out $0.3000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
131.1K
Partial I/O overlap
Overall 60%
Llama-3.3-70B-Instruct
hyperbolic
In $0.1200 / 1M tokens
Out $0.3000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
131.1K
Partial I/O overlap
Overall 60%
Meta-Llama-3-70B-Instruct
hyperbolic
In $0.1200 / 1M tokens
Out $0.3000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
131.1K
Partial I/O overlap
Overall 60%
hermes3-70b
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In $0.1200 / 1M tokens
Out $0.3000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
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Overall 60%
llama3.1-70b-instruct-fp8
lambda_ai
In $0.1200 / 1M tokens
Out $0.3000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
131.1K
Same provider
Overall 60%
llama3.3-70b-instruct-fp8
lambda_ai
In $0.1200 / 1M tokens
Out $0.3000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
131.1K
Same provider
Overall 60%
lfm-40b
lambda_ai
In $0.1000 / 1M tokens
Out $0.2000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
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Overall 55%
deepseek-v3-0324
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In $0.2000 / 1M tokens
Out $0.6000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
131.1K
Same provider
Overall 51%
Hermes-3-Llama-3.1-70B
hyperbolic
In $0.1200 / 1M tokens
Out $0.3000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
32.8K
Partial I/O overlap
Overall 49%
Qwen2.5-Coder-32B-Instruct
hyperbolic
In $0.1200 / 1M tokens
Out $0.3000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
32.8K
Partial I/O overlap
Overall 49%
Llama-3.2-3B-Instruct
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In $0.1200 / 1M tokens
Out $0.3000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
32.8K
Partial I/O overlap
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qwen25-coder-32b-instruct
lambda_ai
In $0.0500 / 1M tokens
Out $0.1000 / 1M tokens
Output: unknown
Function callingParallel function callingTool choiceSystem messages
131.1K
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Overall 47%
gpt-audio-mini-2025-10-06
Azure
In $0.6000 / 1M tokens
Out $2.4000 / 1M tokens
Output: audio, text
Function callingParallel function callingTool choiceSystem messages
16.4K
Partial I/O overlap
Overall 30%
gpt-audio-2025-08-28
Azure
In $2.5000 / 1M tokens
Out $10.0000 / 1M tokens
Output: audio, text
Function callingParallel function callingTool choiceSystem messages
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Partial I/O overlap
Overall 28%
gpt-audio-1.5-2026-02-23
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In $2.5000 / 1M tokens
Out $10.0000 / 1M tokens
Output: audio, text
Function callingParallel function callingTool choiceSystem messages
16.4K
Partial I/O overlap
Overall 28%
gpt-4o-audio-preview-2024-12-17
Azure
In $2.5000 / 1M tokens
Out $10.0000 / 1M tokens
Output: audio, text
Function callingParallel function callingTool choiceSystem messages
16.4K
Partial I/O overlap
Overall 28%
gpt-4o-mini-audio-preview-2024-12-17
Azure
In $2.5000 / 1M tokens
Out $10.0000 / 1M tokens
Output: audio, text
Function callingParallel function callingTool choiceSystem messages
16.4K
Partial I/O overlap
Overall 28%