LLM Pricing
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Qwen2.5-VL-72B-Instruct

nebius · chat model

Qwen2.5-VL-72B-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
$0.1300 / 1M tokens
Output
$0.4000 / 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
$0.1300 / 1M tokens
Output
$0.4000 / 1M tokens
Embedding
$0.1300 / 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 74.2% accuracy Base model: Qwen2.5 (Qwen2.5-7B-Instruct) 2026-05-31 Link
MMLU 83.3% accuracy Base model: Qwen2.5 (Qwen2.5-32B-Instruct) 2026-05-31 Link
MATH 49.8% accuracy Base model: Qwen2.5 (Qwen2.5-7B-Instruct) 2026-05-31 Link
MATH 57.7% accuracy Base model: Qwen2.5 (Qwen2.5-32B-Instruct) 2026-05-31 Link
HumanEval 57.9% pass@1 Base model: Qwen2.5 (Qwen2.5-7B-Instruct) 2026-05-31 Link
HumanEval 58.5% pass@1 Base model: Qwen2.5 (Qwen2.5-32B-Instruct) 2026-05-31 Link
Artificial Analysis Coding Index 11.9 score Base model: qwen2.5 (qwen/qwen-2.5-72b-instruct) 2026-05-31 Link
GPQA Diamond 49.1% accuracy Base model: qwen2.5 (qwen/qwen-2.5-72b-instruct) 2026-05-31 Link
Humanity's Last Exam 4.2% accuracy Base model: qwen2.5 (qwen/qwen-2.5-72b-instruct) 2026-05-31 Link
IFBench 36.9% accuracy Base model: qwen2.5 (qwen/qwen-2.5-72b-instruct) 2026-05-31 Link
SciCode 26.7% accuracy Base model: qwen2.5 (qwen/qwen-2.5-72b-instruct) 2026-05-31 Link
MMMU 70 score Base model: Qwen2.5-VL (Qwen2.5-VL-32B) 2026-05-31 Link
MMMU-Pro 49.5 score Base model: Qwen2.5-VL (Qwen2.5-VL-32B) 2026-05-31 Link
MMStar 69.5 score Base model: Qwen2.5-VL (Qwen2.5-VL-32B) 2026-05-31 Link
MathVista 74.7 score Base model: Qwen2.5-VL (Qwen2.5-VL-32B) 2026-05-31 Link
MathVision 40.0 score Base model: Qwen2.5-VL (Qwen2.5-VL-32B) 2026-05-31 Link
CC-OCR 77.1 score Base model: Qwen2.5-VL (Qwen2.5-VL-32B) 2026-05-31 Link
DocVQA 94.8 score Base model: Qwen2.5-VL (Qwen2.5-VL-32B) 2026-05-31 Link
InfoVQA 83.4 score Base model: Qwen2.5-VL (Qwen2.5-VL-32B) 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
Qwen2.5-VL-72B-Instruct
nebius
In $0.1300 / 1M tokens
Out $0.4000 / 1M tokens
Output: unknown
VisionFunction calling
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-VL-72B-Instruct
nebius
In $0.1300 / 1M tokens
Out $0.4000 / 1M tokens
Output: unknown
VisionFunction calling
131.1K
Same provider
Overall 60%
gemma-3-27b-it
nebius
In $0.0600 / 1M tokens
Out $0.2000 / 1M tokens
Output: unknown
VisionFunction calling
128.0K
Same provider
Overall 49%
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 47%
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 47%
Qwen2.5-72B-Instruct
nebius
In $0.1300 / 1M tokens
Out $0.4000 / 1M tokens
Output: unknown
Function calling
128.0K
Same provider
Overall 47%
mistral-small-2503
azure_ai
In $0.1000 / 1M tokens
Out $0.3000 / 1M tokens
imagetext
Output: text
VisionFunction calling
128.0K
Partial I/O overlap
Overall 46%
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 45%
Llama-3.3-70B-Instruct-Turbo
deepinfra
In $0.1300 / 1M tokens
Out $0.3900 / 1M tokens
text
Output: text
Function calling
131.1K
Partial I/O overlap
Overall 43%
gpt-4o-mini
gmi
In $0.1500 / 1M tokens
Out $0.6000 / 1M tokens
Output: unknown
VisionFunction calling
16.4K
Partial I/O overlap
Overall 42%
meta.llama3-2-11b-instruct-v1:0
Bedrock
In $0.3500 / 1M tokens
Out $0.3500 / 1M tokens
imagetext
Output: text
VisionFunction calling
4.1K
Partial I/O overlap
Overall 38%
us.meta.llama3-2-11b-instruct-v1:0
Bedrock
In $0.3500 / 1M tokens
Out $0.3500 / 1M tokens
imagetext
Output: text
VisionFunction calling
4.1K
Partial I/O overlap
Overall 38%
llama-3.3-70b-instruct
novita
In $0.1350 / 1M tokens
Out $0.4000 / 1M tokens
text
Output: text
Function calling
120.0K
Partial I/O overlap
Overall 38%
gemini-3-flash-preview
gmi
In $0.5000 / 1M tokens
Out $3.0000 / 1M tokens
Output: unknown
VisionFunction calling
65.5K
Partial I/O overlap
Overall 36%
claude-sonnet-4.5
gmi
In $3.0000 / 1M tokens
Out $15.0000 / 1M tokens
Output: unknown
VisionFunction calling
32.0K
Partial I/O overlap
Overall 29%
claude-sonnet-4
gmi
In $3.0000 / 1M tokens
Out $15.0000 / 1M tokens
Output: unknown
VisionFunction calling
32.0K
Partial I/O overlap
Overall 29%
claude-opus-4.5
gmi
In $5.0000 / 1M tokens
Out $25.0000 / 1M tokens
Output: unknown
VisionFunction calling
32.0K
Partial I/O overlap
Overall 29%
claude-opus-4
gmi
In $15.0000 / 1M tokens
Out $75.0000 / 1M tokens
Output: unknown
VisionFunction calling
32.0K
Partial I/O overlap
Overall 29%
gpt-4o
gmi
In $2.5000 / 1M tokens
Out $10.0000 / 1M tokens
Output: unknown
VisionFunction calling
16.4K
Partial I/O overlap
Overall 28%