Model profile
Alibaba

Qwen: Qwen3 Max

Qwen: Qwen3 Max is a budget-priced text-first model from Alibaba with heavy runtime profile, large context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsHeavy latencyLarge contextBudget pricing
Intelligence
31.4

Benchmark blend

Coding
26.4

Dev workflow signal

Context
262K Tokens

Large

Input Price
$1.20

Budget tier

Decision snapshot
50

Qwen: Qwen3 Max currently reads as a budget text-first option with large context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Heavy
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
31.4
31

General reasoning and benchmark headroom.

Limited
Speed
33 tok/s
41

TTFT 1.84s

Limited
Context
262K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$1.20
86

$6.00 output / 1M

Efficient

Editorial Profile

Qwen: Qwen3 Max 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 26Math score 81

Qwen3-Max is an updated release built on the Qwen3 series, offering major improvements in reasoning, instruction following, multilingual support, and long-tail knowledge coverage compared to the January 2025 version. It...

Identity

Alibaba text-first profile

Positioning

Long-context research / Agent workflows with large context and heavy 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 profile is better for deliberate runs than rapid back-and-forth chat.

  • 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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Context
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Context
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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
31.4
MMLU Pro
84.1%
GPQA
76.4%
HLE
11.1%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
26.4
LiveCodeBench
0.767
SciCode
38.3%
Math

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

Math Index
80.7
AIME 2025
80.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
44.1%
TAU2
74.3%
TerminalBench Hard
20.5%
LCR
46.7%

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
262K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Qwen3
Max Completion
32768
Moderation
No
Supported Parameters
max_tokenspresence_penaltyresponse_formatseedtemperaturetool_choicetoolstop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$1.20
Output
per 1M output tokens
$6.00
Blended
AA 3:1 mix
$2.40

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

OR Cache Read
$0.00

Metadata

Raw source tables at the end

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