Model comparison

GPT-6 Astra vs Qwen 3.8 Max

OpenAI against Qwen, compared on context, price, and attributed benchmark results.

Recorded profile verification October 2, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below

OpenAI

GPT-6 Astra

Frontier

Qwen

Qwen 3.8 Max

Frontier

AI model capability comparison
Specification	GPT-6 Astra	Qwen 3.8 Max
Provider
Provider	OpenAI	Qwen
Tier
Tier	Frontier	Frontier
Context window
Context window	Winner: 1.05M	1M
Max output
Max output	128K	Winner: 131K
Input / 1M tokens
Input / 1M tokens	$10	Winner: $2
Output / 1M tokens
Output / 1M tokens	$50	Winner: $6
Weights
Weights	Closed	Closed
Parameters
Parameters	Not verifiedUnverified	2.4T total (MoE; managed active count not verified)
Reasoning levels
Reasoning levels	low, medium, high, xhigh, max	low, medium, xhigh
Modalities
Modalities	text, image	text, image, video
API model id
API model id	gpt-6-astra	qwen3.8-max
Released
Released	September 3, 2026	August 3, 2026
Artificial Analysis Intelligence Index [max] · source date GPT-6 Astra: 2026-10-01
Artificial Analysis Intelligence Index [max] · source date GPT-6 Astra: 2026-10-01	53	Not verifiedUnverified
Artificial Analysis Intelligence Index · source date Qwen 3.8 Max: 2026-10-01
Artificial Analysis Intelligence Index · source date Qwen 3.8 Max: 2026-10-01	Not verifiedUnverified	45
DeepSWE 1.1 · source date GPT-6 Astra: 2026-09-03
DeepSWE 1.1 · source date GPT-6 Astra: 2026-09-03	74.1	Not verifiedUnverified
OSWorld 2.0 (offline) · source date GPT-6 Astra: 2026-09-03
OSWorld 2.0 (offline) · source date GPT-6 Astra: 2026-09-03	72.6	Not verifiedUnverified
GPQA Diamond · source date GPT-6 Astra: 2026-09-03
GPQA Diamond · source date GPT-6 Astra: 2026-09-03	96	Not verifiedUnverified
GPQA Diamond · source date Qwen 3.8 Max: 2026-10-01
GPQA Diamond · source date Qwen 3.8 Max: 2026-10-01	Not verifiedUnverified	92.6
Humanity's Last Exam · source date GPT-6 Astra: 2026-09-03, Qwen 3.8 Max: 2026-08-14 (comparability not verified)
Humanity's Last Exam · source date GPT-6 Astra: 2026-09-03, Qwen 3.8 Max: 2026-08-14 (comparability not verified)	57.2	56.2
Terminal-Bench 2.1 · source date Qwen 3.8 Max: 2026-10-01
Terminal-Bench 2.1 · source date Qwen 3.8 Max: 2026-10-01	Not verifiedUnverified	86.6

Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

Source date2026-10-01
Qwen official checkpoint card reproducing the managed Max evaluation table (vendor) (Managed Qwen3.8-Max column; complete run configuration unverified)
Graduate-level science reasoning: multiple-choice questions written by domain PhDs.
Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.
Source date2026-10-01
Qwen official checkpoint card reproducing the managed Max evaluation table (vendor) (Managed Qwen3.8-Max column; Claude Code avg@10, 5-hour timeout, max_tokens 131,072; exact run date unverified)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.
Source date2026-10-01
Artificial Analysis (Qwen3.8 Max (0902))
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
Source date2026-10-01
Artificial Analysis (GPT-6 Astra (max))
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
Source date2026-09-03
OpenAI GPT-6 Astra evaluation table (vendor-reported, with tools; maximum across evaluated efforts)
Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.
Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
Source date2026-09-03
OpenAI GPT-6 Astra evaluation table (vendor-reported; maximum across evaluated efforts)
Graduate-level science reasoning: multiple-choice questions written by domain PhDs.
Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.
Source date2026-09-03
OpenAI (vendor-reported) (v2026.08.08 offline set, partial reward; vendor research/API evaluation, maximum across evaluated efforts)
Computer-use: completing real OS tasks (files, apps, settings) in a sandboxed desktop.
Comparable with caveatOffline vs live interaction modes are NOT comparable; step budgets and screen resolution must match.
Source date2026-09-03
OpenAI (xhigh, mini-swe-agent; matches Datacurve's independent board (74.1% at $4.43/task, rank 1; board updated 2026-09-22))
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
Source date2026-08-14
Z.ai GLM-5.3 vendor evaluation of Qwen3.8-Max, with tools; distinct from Qwen no-tools results
Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.
Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.

Pricing tiers

GPT-6 Astra: Standard $10/$50 per MTok (≤272K input); above 272K the full request bills $20/$75. Cached input $1/$2 and cache writes $12.50/$25 for short/long context. Batch/Flex are half Standard; Fast is 2x. Ultrafast is 6x: $60/$300 short, $120/$450 long, through Responses with global/US processing; no EU or other regional inference residency. Regional/FedRAMP processing adds 10% where eligible. Knowledge cutoff Apr 30 2026. Enterprise model access is disabled by default.

Qwen 3.8 Max: Model Studio International $2/$6 per MTok; implicit cache $0.25, explicit creation $2.50/read $0.17. China/Global $1.65/$4.951, cache $0.206, creation $2.063/read $0.137. Output 131,072 in both modes; input 991,808 non-thinking or 983,616 thinking, 1M context and 262,144 reasoning. Batch supported in China, unavailable in International/Global.

FrontierRecord checked October 1, 2026

GPT-6 Astra

OpenAI describes GPT-6 Astra as its most capable model for complex reasoning, coding and computer use, and the first it gates at the Preparedness Framework's 'Critical' cybersecurity threshold.

Best for

Computer-use agents
Agentic coding
Hard reasoning

Watch out

OpenAI places Astra at its Critical cybersecurity threshold and disables it by default for Enterprise. Tool calling requires Responses; none reasoning and custom temperature/top_p/logprobs are unsupported. Long prompts above 272K increase the full-request rate. GPT-6.1 Sol has lower base token prices; compare results on your own workload.

FrontierRecord checked October 2, 2026

Qwen 3.8 Max

Qwen 3.8 Max is Alibaba's managed multimodal flagship, with a vendor-described 2.4T MoE scale and 1M context.

Best for

Hosted agentic work
Long-context
Multimodal

Watch out

The text-only open checkpoint has 262,144 native context with extension and requires thinking; its 95B active count is not verified for the managed API. Managed Max accepts visual input and supports thinking on/off, xhigh default and low/medium/xhigh controls, with preserved thinking by default. API reasoning_effort and thinking_budget cannot both be set. Regional web-search and Batch support differ. Independent AA 0902 results identify a snapshot, not every alias version.

Benchmark

DeepSWE 1.1 in context

Only GPT-6 Astra has a published DeepSWE 1.1 result. It is shown against the wider field, with cost per completed task alongside the score.

Local leader

GPT-6 Astra [xhigh]

74.1%

Rows shown

Best source-matched Pass@1 per model

Snapshot date

2026-10-01

Mirrored from deepswe.datacurve.ai

Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost, output tokens and steps. Legacy v1 transcriptions are excluded here until an exact primary receipt is retained.

DeepSWE 1.1 leaderboard with pass rate, cost, tokens, and steps per task
#	Model	Pass@1	Cost / task	Tokens / task	Steps / task
1	GPT-6 Astra [xhigh]	74.1%	$4.43	30k	29
2	Gemini 3.8 Flash [high]	73.8%	$2.36	143k	166
3	Claude Opus 5 [max]	73.6%	$11.84	118k	99
4	GPT-5.6 Sol [max]	72.7%	$8.39	60k	61
5	Claude Fable 5 [xhigh]	69.9%	$13.41	80k	68
6	GPT-5.6 Terra [max]	69.6%	$4.95	72k	76
7	GLM 5.3 [max]	69.0%	$3.99	80k	124
8	Kimi K3 [max]	68.5%	$4.65	82k	98
9	Grok 4.6 [medium]	67.5%	$3.45	50k	70
10	GPT-5.6 Luna [max]	67.2%	$3.03	73k	102
11	GPT-5.5 [xhigh]	67.0%	$7.23	46k	82
12	Gemini 3.7 Flash [medium]	65.5%	$2.03	94k	117
13	GLM 5.3 Flash [max]	63.4%	$0.48	73k	123
14	DeepSeek V4-Pro [max]	62.8%	$0.24	106k	155
15	Claude Opus 4.8 [max]	59.0%	$13.22	135k	120
16	Qwen3.8-Max [xhigh]	57.5%	$3.73	95k	111
17	Muse Spark 1.2 [xhigh]	54.9%	$3.70	99k	101
18	Claude Sonnet 5 [max]	53.8%	$26.40	214k	268
19	Grok 4.5 [high]	53.8%	$2.42	36k	61
20	DeepSeek V4-Flash [max]	53.3%	$0.10	108k	153
21	Muse Spark 1.1 [xhigh]	53.3%	$2.36	74k	96
22	GPT-5.4 [xhigh]	51.8%	$5.65	71k	70
23	Gemini 3.6 Flash [high]	46.7%	$4.42	96k	117
24	GLM 5.2 [max]	43.8%	$3.92	78k	129
25	Gemini 3.5 Flash [high]	36.1%	$3.45	76k	105
26	Kimi K2.7 Code	30.5%	$2.82	59k	149
27	Claude Sonnet 4.6 [high]	29.9%	$5.52	76k	134
28	Gemini 3.1 Pro [high]	11.7%	$2.14	28k	76

“Best per model” shows each model’s highest source-matched Pass@1 in this capture; switch to all effort levels to see every retained configuration. Efforts differ across models, so this view is not a comparison at matched effort. The source was generated September 22 and captured October 1. Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.

When the cheaper one wins

Qwen 3.8 Max is cheaper on output at $6 per million tokens against $50 for GPT-6 Astra — about 8.3×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. On DeepSWE 1.1, Qwen 3.8 Max is 57.5% Pass@1 at $3.73/task versus GPT-6 Astra at 74.1% / $4.43/task. This compares standard short-context token rates, not total cost per accepted task. Include input, cache, context tiers, schedules, retries and review in your pilot.

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Evidence confidence: Medium

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

Pricing verified on both sides — Input and output rates are verified for both models.
4/5 core specs verified on both sides — Not published for at least one side: parameter count.
No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
Verified within the last 90 days — Older of the two catalog checks was 25 days ago.
Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

GPT-6 Astra: OpenAI model card — whole-profile source check October 1 (accessed 2026-10-01)
GPT-6 Astra: OpenAI current API pricing and processing tiers (accessed 2026-10-01)
GPT-6 Astra: OpenAI API changelog — September 3 release; checked October 1 (accessed 2026-10-01)
GPT-6 Astra: OpenAI — Introducing GPT-6 Astra (accessed 2026-09-03)
GPT-6 Astra: OpenAI API pricing (gpt-6-astra $10/$50) (accessed 2026-09-03)
GPT-6 Astra: CNBC — OpenAI launches GPT-6 Astra (accessed 2026-09-03)
GPT-6 Astra: OpenAI model docs — gpt-6-astra (128K max output, effort levels) (accessed 2026-09-05)
GPT-6 Astra: OpenAI API pricing (gpt-6-astra ≤272K $10/$50, >272K $20/$75) (accessed 2026-09-26)
Qwen 3.8 Max: Dedicated exact capacities and regional pricing (accessed 2026-10-01)
Qwen 3.8 Max: Exact effort and compatibility rules (accessed 2026-10-01)
Qwen 3.8 Max: Current managed card and exact cache rates (accessed 2026-10-01)
Qwen 3.8 Max: Current hosted thinking versus checkpoint mode (accessed 2026-10-01)
Qwen 3.8 Max: Qwen — Qwen 3.8 Max (accessed 2026-08-29)
Qwen 3.8 Max: Alibaba Cloud Model Studio (accessed 2026-08-29)
Qwen 3.8 Max: Alibaba Cloud Model Studio — text generation models (1M ctx, 131,072 out) (accessed 2026-09-26)
Qwen 3.8 Max: Qwen3.8-2.4T-A95B checkpoint card (distinguishes enhanced managed Max) (accessed 2026-10-01)
Qwen 3.8 Max: QwenCloud managed Qwen3.8-Max model card (cache and mode-specific limits) (accessed 2026-10-01)

Related comparisons

Diving deeper on one model? GPT-6 Astra · Qwen 3.8 Max

Common questions

GPT-6 Astra vs Qwen 3.8 Max

Answered from the verified figures on this page rather than general guidance.

Is GPT-6 Astra or Qwen 3.8 Max cheaper for input?

Qwen 3.8 Max is cheaper at $2 per million input tokens, against $10 for GPT-6 Astra — roughly 5.0× the price. Estimate both input and output token volume for your workload. These are base rates; GPT-6 Astra: Standard $10/$50 per MTok (≤272K input); above 272K the full request bills $20/$75. Cached input $1/$2 and cache writes $12.50/$25 for short/long context. Batch/Flex are half Standard; Fast is 2x. Ultrafast is 6x: $60/$300 short, $120/$450 long, through Responses with global/US processing; no EU or other regional inference residency. Regional/FedRAMP processing adds 10% where eligible. Knowledge cutoff Apr 30 2026. Enterprise model access is disabled by default. Qwen 3.8 Max: Model Studio International $2/$6 per MTok; implicit cache $0.25, explicit creation $2.50/read $0.17. China/Global $1.65/$4.951, cache $0.206, creation $2.063/read $0.137. Output 131,072 in both modes; input 991,808 non-thinking or 983,616 thinking, 1M context and 262,144 reasoning. Batch supported in China, unavailable in International/Global.

Is GPT-6 Astra or Qwen 3.8 Max cheaper for output?

Qwen 3.8 Max is cheaper at $6 per million output tokens, against $50 for GPT-6 Astra — roughly 8.3× the price. Estimate both input and output token volume for your workload. These are base rates; GPT-6 Astra: Standard $10/$50 per MTok (≤272K input); above 272K the full request bills $20/$75. Cached input $1/$2 and cache writes $12.50/$25 for short/long context. Batch/Flex are half Standard; Fast is 2x. Ultrafast is 6x: $60/$300 short, $120/$450 long, through Responses with global/US processing; no EU or other regional inference residency. Regional/FedRAMP processing adds 10% where eligible. Knowledge cutoff Apr 30 2026. Enterprise model access is disabled by default. Qwen 3.8 Max: Model Studio International $2/$6 per MTok; implicit cache $0.25, explicit creation $2.50/read $0.17. China/Global $1.65/$4.951, cache $0.206, creation $2.063/read $0.137. Output 131,072 in both modes; input 991,808 non-thinking or 983,616 thinking, 1M context and 262,144 reasoning. Batch supported in China, unavailable in International/Global.

Which has the larger context window, GPT-6 Astra or Qwen 3.8 Max?

GPT-6 Astra accepts 1.05M tokens against 1M for Qwen 3.8 Max. This only matters if you routinely send very long documents or large codebases.

Do GPT-6 Astra and Qwen 3.8 Max support the same reasoning levels?

GPT-6 Astra exposes low, medium, high, xhigh, max, while Qwen 3.8 Max exposes low, medium, xhigh.

Should I use GPT-6 Astra or Qwen 3.8 Max?

Both are classified in our frontier tier; this editorial grouping does not imply equal capability. GPT-6 Astra is a candidate for computer-use agents; Qwen 3.8 Max is a candidate for hosted agentic work. Compare accuracy, tool reliability, latency and cost on representative tasks.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

Input price: Qwen 3.8 Max costs $2 per 1M tokens versus $10 for GPT-6 Astra — a 5x difference at the headline tier.
Context: GPT-6 Astra takes 1.05M against 1M for Qwen 3.8 Max — only decisive if your prompts approach the smaller window.

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