LLM Pricing
← All models

Llama-3.1-Nemotron-Ultra-253B-v1

nebius · Chat model

Llama-3.1-Nemotron-Ultra-253B-v1 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.

Provider and model identifiers are kept in their original form for accuracy.

nebius-nebius-nvidia-llama-3-1-nemotron-ultra-253b-v1

Input
$0.6000 / 1M tokens
Output
$1.8000 / 1M tokens
Cached input
N/A
Context window
128.0K

Catalog generated: Jul 13, 2026

Compare this model Open a side-by-side table using this route and a close match from the current catalog. Estimate cost Use token counts, request volume, and cache assumptions to check a realistic monthly bill.

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
$0.6000 / 1M tokens
Output
$1.8000 / 1M tokens
Embedding
$0.6000 / 1M tokens

Limits

Context window
128.0K
Max input tokens
128.0K
Max output tokens
128.0K
Max tokens
128.0K
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. API vs subscription Learn when API pricing matters compared with ChatGPT, Claude Code, Cursor, or Copilot subscriptions. Cache and batch pricing Understand cached input, cache write, batch, priority, and flex pricing rows shown on this page.

Capabilities

Capability Supported
Vision -
Function calling Supported
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 dataLiteLLM model cost map
Synced at2026-05-28
Catalog generated2026-07-13T14:21:55.110Z