Model comparison
GPT-6 Astra vs Qwen 3.8 Max
OpenAI against Qwen, compared on context, price, and verified benchmark results.
Catalog record checked September 5, 2026Individual provider fields may changeEvidence confidence: High — see receipts below
OpenAI
GPT-6 Astra
Frontier
Qwen
Qwen 3.8 Max
Frontier · Open weights
| Specification | GPT-6 Astra | Qwen 3.8 Max |
|---|---|---|
| Provider | OpenAI | Qwen |
| Tier | Frontier | Frontier |
| Context window | Winner: 1.05M | 991K |
| Max output | 128K | Winner: 131K |
| Input / 1M tokens | $10 | Winner: $2 |
| Output / 1M tokens | $50 | Winner: $6 |
| Weights | Closed | Open |
| Parameters | computer-use flagship | 2.4T total / 95B active (MoE) |
| Reasoning levels | low, medium, high, xhigh, max | low, high, max |
| Modalities | text, image | text, image, video |
| API model id | gpt-6-astra | qwen3.8-max |
| Released | September 3, 2026 | August 3, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-09-03) | 61.2 | Not verifiedUnverified |
| Artificial Analysis Intelligence Index (2026-08-14) | Not verifiedUnverified | 58 |
| DeepSWE 1.1 (2026-09-03) | 74.1 | Not verifiedUnverified |
| OSWorld 2.0 (offline) (2026-09-03) | 72.6 | Not verifiedUnverified |
| GPQA Diamond (2026-09-03) | Winner: 96 | 92.6 |
| Humanity's Last Exam (2026-09-03) | Winner: 57.2 | 56.2 |
| Terminal-Bench 2.1 (2026-09-05) | Winner: 89.9 | 86.6 |
| SWE-bench Verified (2026-09-01) | Not verifiedUnverified | 85.6 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
- 2026-09-05: Artificial Analysis Terminal-Bench v2.1 (independent, Terminus 2 harness — conflicts with tbench.ai 87.4; harness difference preserved)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
- 2026-09-03: Terminal-Bench official leaderboard (independent, native harness, high effort; captured 2026-09-03 — the live board has since moved to Terminal-Bench 4.0)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
- 2026-09-03: OpenAI GPT-6 Astra launch chart (vendor, with tools; transcribed by Vellum)Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task. Comparability: directly comparable
- 2026-09-01: vals.ai SWE-bench Verified leaderboard (independent, mini-swe-agent bash-only); not published by Qwen (official used SWE-bench Pro 67.7)Real GitHub issue resolution: does the model's patch pass the hidden tests. Comparability: comparable with caveat — Post-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
- 2026-08-14: Z.ai GLM-5.3 blog (independent Z.ai-run, with tools; Qwen official no-tools 43.6 — both preserved)Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise. Comparability: comparable with caveat — Subset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
- 2026-08-14: Artificial AnalysisComposite index blending reasoning, knowledge, and coding evals into one 0–100 score. Comparability: directly comparable — AA occasionally rebaselines the index scale between snapshots — a score captured on one date is only comparable to same-snapshot scores (check measuredAt).
- 2026-08-03: Qwen official blog (vendor-run table)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
Pricing tiers: GPT-6 Astra: Standard $10/$50 per MTok (≤272K context); >272K input billed $20/$75 (Fast mode output $100). Cached input $1/MTok; Fast mode is 2x price for ~2.5x speed. Knowledge cutoff Apr 30 2026. Rolling out over several days from 2026-09-03; Enterprise tenants get it disabled by default. · Qwen 3.8 Max: $2/$6 per MTok (Alibaba Cloud Model Studio). Weights announced open, verify they have dropped.
GPT-6 Astra
GPT-6 Astra is OpenAI's computer-use flagship — the first model it gates at the Preparedness Framework's 'Critical' cybersecurity threshold, trained on >100,000 GPUs at Stargate.
Best for
- Computer-use agents
- Agentic coding
- Hard reasoning
Watch out
Gated at the 'Critical' cyber threshold and disabled by default for Enterprise; long-context work bills $20/$75 above 272K input tokens.
Qwen 3.8 Max
Qwen 3.8 Max is Alibaba's flagship — 2.4T MoE with 1M-class context, near frontier on the Intelligence Index.
Best for
- Open-weight frontier work
- Long-context
- Multimodal
Watch out
Open weights dropped 2026-08-12 under a custom (non-Apache) licence with vision and 1M-context stripped from the open checkpoint — the open checkpoint is not the full API model. Verify the licence before commercial use.
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. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerEvidence confidence: High
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.
- 5/5 core specs verified on both sides — All core specifications verified for both models.
- 5 shared named benchmarks with differing scores — Measured on: Artificial Analysis Intelligence Index, GPQA Diamond, Humanity's Last Exam, Terminal-Bench 2.1, Terminal-Bench 2.1.
- Verified within the last 90 days — Newest catalog check was 6 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 — 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)
- 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)
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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. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-6 Astra has tiered pricing: Standard $10/$50 per MTok (≤272K context); >272K input billed $20/$75 (Fast mode output $100). Cached input $1/MTok; Fast mode is 2x price for ~2.5x speed. Knowledge cutoff Apr 30 2026. Rolling out over several days from 2026-09-03; Enterprise tenants get it disabled by default. Qwen 3.8 Max has tiered pricing: $2/$6 per MTok (Alibaba Cloud Model Studio). Weights announced open, verify they have dropped.
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. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GPT-6 Astra has tiered pricing: Standard $10/$50 per MTok (≤272K context); >272K input billed $20/$75 (Fast mode output $100). Cached input $1/MTok; Fast mode is 2x price for ~2.5x speed. Knowledge cutoff Apr 30 2026. Rolling out over several days from 2026-09-03; Enterprise tenants get it disabled by default. Qwen 3.8 Max has tiered pricing: $2/$6 per MTok (Alibaba Cloud Model Studio). Weights announced open, verify they have dropped.
Which has the larger context window, GPT-6 Astra or Qwen 3.8 Max?
GPT-6 Astra accepts 1.05M tokens against 991K 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, high, max.
Should I use GPT-6 Astra or Qwen 3.8 Max?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GPT-6 Astra suits computer-use agents; Qwen 3.8 Max suits open-weight frontier work.
Can I self-host GPT-6 Astra or Qwen 3.8 Max?
Qwen 3.8 Max publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. GPT-6 Astra is a closed model whose supported access paths are controlled by its provider.
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 991K for Qwen 3.8 Max — only decisive if your prompts approach the smaller window.
- Measured capability: GPT-6 Astra leads Artificial Analysis Intelligence Index 61.2 to 58 (measured 2026-09-03).
- Deployment: Qwen 3.8 Max publishes weights you can self-host; the other is API-only.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.