Model profile
Alibaba

Qwen: Qwen3 235B A22B

Qwen: Qwen3 235B A22B is a budget-priced text-first model from Alibaba with partial runtime data, extended context posture, and the clearest fit around long-context research / coding.

Best for: Long-context research / CodingN/A latencyExtended contextBudget pricing
Intelligence
10.8

Benchmark blend

Coding
0.343

Dev workflow signal

Context
131K Tokens

Extended

Input Price
$0.70

Budget tier

Decision snapshot
43

Qwen: Qwen3 235B A22B currently reads as a budget text-first option with extended context and a partially published runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Coding
Latency tier
N/A
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
10.8
11

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
131K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.70
86

$2.80 output / 1M

Efficient

Editorial Profile

Qwen: Qwen3 235B A22B in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 34Math score 24

Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and...

Identity

Alibaba text-first profile

Positioning

Long-context research / Coding with extended context and partially published runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit
  • Long-context summarization, repo analysis, and policy or document review.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
10.8
MMLU Pro
76.2%
GPQA
61.3%
HLE
4.2%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.343
SciCode
29.9%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
23.7
AIME
32.7%
AIME 2025
23.7%
Math 500
90.2%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
36.6%
TAU2
27.2%
TerminalBench Hard
6.1%
LCR
0.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
131K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Qwen3
Max Completion
8192
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoningmax_tokenspresence_penaltyreasoningresponse_formatseedstoptemperaturetool_choicetoolstop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.70
Output
per 1M output tokens
$2.80
Blended
AA 3:1 mix
$1.23

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.