Qwen2.5-32B-Instruct

nebius · chat model

Qwen2.5-32B-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

$0.0600 / 1M tokens

Output

$0.2000 / 1M tokens

Cached input

N/A

Context

128.0K

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	$0.0600 / 1M tokens
Output	$0.2000 / 1M tokens
Embedding	$0.0600 / 1M tokens

Token limits

Context window

128.0K

Max input tokens

128.0K

Max output tokens

128.0K

Max tokens

128.0K

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

Sources

Source links	LiteLLM repository LiteLLM model cost map
Pricing data	LiteLLM model cost map
Synced at	2026-05-28

Docs

Official docs

Model 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
Qwen2.5-32B-Instruct nebius	In $0.0600 / 1M tokens Out $0.2000 / 1M tokens	Output: unknown	Function calling	128.0K	Current model Reference row

Model	Cost	Input shape	Features	Context	Why it is close
Mistral-Nemo-Instruct-2407 nebius	In $0.0400 / 1M tokens Out $0.1200 / 1M tokens	Output: unknown	Function calling	128.0K	Same provider Overall 53%
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 51%
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 50%
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 50%
Qwen2.5-72B-Instruct nebius	In $0.1300 / 1M tokens Out $0.4000 / 1M tokens	Output: unknown	Function calling	128.0K	Same provider Overall 50%
gemma-3-27b-it nebius	In $0.0600 / 1M tokens Out $0.2000 / 1M tokens	Output: unknown	Function calling	128.0K	Same provider Overall 48%
Qwen3-14B nebius	In $0.0800 / 1M tokens Out $0.2400 / 1M tokens	Output: unknown	Function calling	32.8K	Same provider Overall 45%
Qwen3-4B nebius	In $0.0800 / 1M tokens Out $0.2400 / 1M tokens	Output: unknown	Function calling	32.8K	Same provider Overall 45%
Qwen3-32B nebius	In $0.1000 / 1M tokens Out $0.3000 / 1M tokens	Output: unknown	Function calling	32.8K	Same provider Overall 42%
Phi-4-mini-instruct azure_ai	In $0.0750 / 1M tokens Out $0.3000 / 1M tokens	text Output: text	Function calling	4.1K	Partial I/O overlap Overall 40%
Phi-4-mini-reasoning azure_ai	In $0.0800 / 1M tokens Out $0.3200 / 1M tokens	text Output: text	Function calling	4.1K	Partial I/O overlap Overall 39%
eu.meta.llama3-2-3b-instruct-v1:0 Bedrock	In $0.1900 / 1M tokens Out $0.1900 / 1M tokens	text Output: text	Function calling	4.1K	Partial I/O overlap Overall 39%
Mistral-7B-Instruct-v0.1 anyscale	In $0.1500 / 1M tokens Out $0.1500 / 1M tokens	Output: unknown	Function calling	16.4K	Partial I/O overlap Overall 39%
Mixtral-8x7B-Instruct-v0.1 anyscale	In $0.1500 / 1M tokens Out $0.1500 / 1M tokens	Output: unknown	Function calling	16.4K	Partial I/O overlap Overall 39%
Mixtral-8x22B-Instruct-v0.1 anyscale	In $0.9000 / 1M tokens Out $0.9000 / 1M tokens	Output: unknown	Function calling	65.5K	Partial I/O overlap Overall 36%
mistral-large-2402 Azure	In $8.0000 / 1M tokens Out $24.0000 / 1M tokens	Output: unknown	Function calling	32.0K	Partial I/O overlap Overall 29%
mistral-large-latest Azure	In $8.0000 / 1M tokens Out $24.0000 / 1M tokens	Output: unknown	Function calling	32.0K	Partial I/O overlap Overall 29%
qwen-turbo dashscope	In $0.0500 / 1M tokens Out $0.2000 / 1M tokens	Output: unknown	Function calling	16.4K	Partial I/O overlap Overall 29%
qwen-turbo-2025-04-28 dashscope	In $0.0500 / 1M tokens Out $0.2000 / 1M tokens	Output: unknown	Function calling	16.4K	Partial I/O overlap Overall 29%
qwen-turbo-latest dashscope	In $0.0500 / 1M tokens Out $0.2000 / 1M tokens	Output: unknown	Function calling	16.4K	Partial I/O overlap Overall 29%
qwen-turbo-2024-11-01 dashscope	In $0.0500 / 1M tokens Out $0.2000 / 1M tokens	Output: unknown	Function calling	8.2K	Partial I/O overlap Overall 28%
command-r-plus Azure	In $3.0000 / 1M tokens Out $15.0000 / 1M tokens	text Output: text	Function calling	4.1K	Partial I/O overlap Overall 26%
Qwen2.5-Coder-32B-Instruct nscale	In $0.0600 / 1M tokens Out $0.2000 / 1M tokens	Output: unknown	Low overlap	N/A	Partial I/O overlap Overall 20%