GPT-6 Astra vs Qwen3.8 Max

At a glance

• Capability index

  • GPT-6 Astra166.4
  • Qwen3.8 Max156.4

  GPT-6 Astra is 10.0 points higher.

• Price per 1M tokens

  • GPT-6 Astra$20.00
  • Qwen3.8 Max$3.00

  Qwen3.8 Max is 6.7 times cheaper.

• Context window

  • GPT-6 Astra1.1M tokens
  • Qwen3.8 Max1M tokens

  GPT-6 Astra takes in 5% more.

• Released

  • GPT-6 AstraSep 4, 2026
  • Qwen3.8 MaxAug 3, 2026

  GPT-6 Astra is 32 days newer.

Where each scores higher

On the 22 tests both have taken, each on its own scale. A gap of a point or two can sit within a test’s margin of error.

Level on Arena instruction following.

Everything side by side

Comparison of GPT-6 Astra vs Qwen3.8 Max

Measure	GPT-6 AstraOpenAI	Qwen3.8 MaxAlibaba
Model
Maker	OpenAI	Alibaba
Sold here by	OpenAI	Alibaba
API model ID	gpt-6-astra	qwen3.8-max
Released	Sep 4, 2026	Aug 3, 2026
Knowledge cutoff	Apr 30, 2026	Not reported
Weights	Closed: hosted access only	Closed: hosted access only
Developer’s country	United States	China
Hosts selling it	29including OpenAI directly	36including Alibaba directly
Training compute	1.0 × 10²⁷ FLOPEpoch AI estimate	Not published
Test results
Capability index	166.4 (best of these models)#2 of 274	156.4#22 of 274
LiveBench overall	82.2% (best of these models)#4 of 66	78.5%#17 of 66
Arena text	1477#29 of 413	1482 (best of these models)#23 of 413
LiveBench coding	80.4% (best of these models)#20 of 66	72.9%#52 of 66
Arena WebDev	1788 (best of these models)#2 of 138	1671#10 of 138
Arena coding	1543 (best of these models)#7 of 408	1524#28 of 408
LiveBench mathematics	96.8% (best of these models)#4 of 66	91.3%#27 of 66
Mock AIME 2024–2025	100.0% (best of these models)#1 of 297	99.4%#14 of 297
FrontierMath Tiers 1–3	93.7% (best of these models)#1 of 114	74.7%#18 of 114
FrontierMath Tier 4	97.6% (best of these models)#2 of 70	46.3%#25 of 70
Arena math	1490#27 of 396	1498 (best of these models)#18 of 396
SimpleQA Verified	75.6% (best of these models)#1 of 86	45.8%#37 of 86
LiveBench agentic coding	57.3%#21 of 66	64.7% (best of these models)#7 of 66
DeepSWE	74.1% (best of these models)#1 of 69	57.5%#36 of 69
LiveBench language	89.4% (best of these models)#4 of 66	79.7%#34 of 66
LiveBench instructions	75.6% (best of these models)#8 of 66	74.1%#13 of 66
Arena creative writing	1449#39 of 411	1468 (best of these models)#20 of 411
Arena instruction following	1474#30 of 413	1474#31 of 413
LiveBench data analysis	83.0% (best of these models)#1 of 66	78.4%#24 of 66
LiveBench reasoning	92.7% (best of these models)#1 of 66	88.2%#23 of 66
GPQA Diamond	95.8% (best of these models)#1 of 319	92.7%#24 of 319
Chess puzzles	72.0% (best of these models)#1 of 227	29.0%#54 of 227
Arena hard prompts	1502#30 of 413	1503 (best of these models)#27 of 413
Price per million tokens
Input	$10.00	$2.00 (best of these models)
Output	$50.00	$6.00 (best of these models)
Blended, 3 input to 1 output	$20.00	$3.00 (best of these models)
Cached input	$1.00	$0.25 (best of these models)
Writing to the cache	$12.50	$2.50 (best of these models)
Long requests	$20.00 in, $75.00 outabove 272K tokens	No separate price reported
The maker’s own price	This listing	This listing
Limits
Context window	1.1M tokens (best of these models)	1M tokens
Max input	922K tokens	991K tokens (best of these models)
Max output	128K tokens	131.1K tokens (best of these models)
Features
Reads	images, PDFs and text	images, PDFs, text and video
Produces	text	text
Reasoning	Yeseffort low, medium, high, xhigh, max	Yeseffort low, medium, xhigh; can be switched off; thinking budget can be set
Tool calling	Yes	Yes
Structured output	Yes, such as JSON	Yes, such as JSON
File attachments	Yes	Yes
Temperature setting	Fixed by the provider	Supported

Bold marks the better value in each row. Prices are each model’s own list price, or the middle price across hosts when the maker does not sell it, blended as three input tokens for every output token. Test results as published by LiveBench (Apache-2.0), Epoch AI (CC BY 4.0), Datacurve DeepSWE, compiled by Epoch AI (CC BY 4.0), Arena (CC BY 4.0); model details and prices from models.dev (MIT). Confirm pricing with the provider before use.