GPT-6 Astra wins on coding (99 vs 93) and writing quality. Qwen 3.8 Max wins on price ($2 vs $10/1M input). For most workflows, GPT-6 Astra is the stronger default — openai's frontier answer to fable 5.1 — computer-use and agentic-coding leader at $10/$50.
The safest GPT-6 Astra vs Qwen 3.8 Max default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
GPT-6 Astra is the strongest answer here for GPT-6 Astra vs Qwen 3.8 Max — pick it when quality of output matters more than the $10.00/1M/1M input you pay for it.
Qwen 3.8 Max handles the same job for about 87% less per token. Start here and only move up if the output is not good enough.
Qwen 3.8 Max is the fastest of these for GPT-6 Astra vs Qwen 3.8 Max — worth it when latency is what the reader notices, not the last few points of reasoning depth.
GPT-6 Astra leads on coding with a score of 99 vs 93 for Qwen 3.8 Max.
GPT-6 Astra has the larger context window: 1.05M vs 1M for Qwen 3.8 Max.
Qwen 3.8 Max is cheaper at $2/1M input tokens vs $10/1M for GPT-6 Astra.
Choose GPT-6 Astra for computer and browser use, long-horizon agentic coding, and frontier math and science work. Its coding and research scores are what carry the recommendation here.
Qwen 3.8 Max earns its place when your work is mostly multimodal and vision-heavy workloads at scale, even though it loses the overall count here.
Qwen 3.8 Max is the more cost-efficient option at $2/1M input — GPT-6 Astra costs 5x more per input token, so the gap is worth taking seriously wherever token volume rather than peak quality drives the bill.
Switch the scoring lens to see whether the GPT-6 Astra vs Qwen 3.8 Max answer changes when cost, speed, or long-document depth leads the decision.
OpenAI / Premium / Sep 4, 2026
OpenAI's frontier answer to Fable 5.1 — computer-use and agentic-coding leader at $10/$50.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak.
Where the GPT-6 Astra vs Qwen 3.8 Max recommendation shifts once you weigh price or latency differently.
OpenAI's frontier answer to Fable 5.1 — computer-use and agentic-coding leader at $10/$50.
Best Chinese flagship — beats GPT-5.6 Sol on coding, #2 globally for vision.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GPT-6 AstraOpenAI | $10.00/1M | $50.00/1M | $200 | 1.1M tokens | Deliberate | 100 | 97 | 100 |
| Qwen 3.8 MaxAlibaba | $2.00/1M | $6.00/1M | $32 | 1M tokens | Balanced | 93 | 85 | 88 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for GPT-6 Astra vs Qwen 3.8 Max, what it is genuinely good at, and where we would steer you away from it.
Our pick for GPT-6 Astra vs Qwen 3.8 Max. It scores 100/100 on the coding axis we weight this page by, and nothing else in this shortlist matches it on output quality.
OpenAI's September 3, 2026 frontier release — the first GPT-6 model and OpenAI's answer to Claude Fable 5.1 two days earlier. State of the art on computer use (OSWorld 2.0 72.6% in ~47% less time than GPT-5.6 Sol), agentic coding (Terminal-Bench 4.0 57.9%), and frontier math (FrontierMath Tier 4 97.6%). $10/$50 per 1M tokens, 1.05M context, 128K output, knowledge cutoff April 30, 2026.
You are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak.
The new computer-use and agentic-coding ceiling, and OpenAI's first model priced like a Mythos-class Claude. Astra beats Claude Fable 5.1 on Terminal-Bench 4.0 (57.9% vs 55.8%), Terminal-Bench Science (64.6% vs 52.6%) and FrontierMath Tier 4 (97.6% vs 87.8%), and it is the only model with a credible OSWorld 2.0 result above 70%. It loses to Fable 5.1 on Humanity's Last Exam and on Artificial Analysis's index, costs five times GPT-5.6 Sol, and ships without a SWE-bench number. If your work is computer use, browser agents or math, it is the pick; for everyday coding at scale, Sol at $2/$10 remains the value default.
Full pricing, benchmark table and release notes on the GPT-6 Astra page.
The value option for GPT-6 Astra vs Qwen 3.8 Max: about 87% less per token than GPT-6 Astra, at 93/100 on coding. Worth starting here and moving up only if the output disappoints.
Alibaba's largest model ever — a 2.4-trillion-parameter MoE (95B active) multimodal flagship that beat GPT-5.6 Sol on SWE-bench Pro and ranks #2 globally for vision.
You need independently verified benchmarks or Western data residency.
The strongest Chinese multimodal flagship and a legitimate SWE-bench Pro upset over GPT-5.6 Sol. If vision matters, only Fable 5-class models beat it — at 3–8x the price. Wait for independent evals before betting production on the self-reported numbers.
Full pricing, benchmark table and release notes on the Qwen 3.8 Max page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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GPT-6 Astra wins on more of the categories we score — coding, research, long context — so it is the better default of the two. Qwen 3.8 Max is the better pick when your work is mostly multimodal and vision-heavy workloads at scale. Neither is universally "better": GPT-6 Astra is aimed at computer and browser use and long-horizon agentic coding, Qwen 3.8 Max at multimodal and vision-heavy workloads at scale.
Qwen 3.8 Max is cheaper at $2/1M input and $6/1M output. GPT-6 Astra costs $10/1M input and $50/1M output.
GPT-6 Astra has the larger context window at 1.05M tokens vs Qwen 3.8 Max's 1M. For large document analysis, GPT-6 Astra is the stronger pick.
GPT-6 Astra is better for coding with a score of 99 vs Qwen 3.8 Max's 93 (out of 100). Claude Opus 5.5 is the overall coding leader in this directory at 100/100.
Qwen 3.8 Max is faster with a balanced speed rating (score: 3) vs GPT-6 Astra's deliberate rating (score: 2). Speed matters most for interactive and high-throughput work; for batch jobs the GPT-6 Astra latency penalty is usually invisible.
$10/$50 per 1M — two and a half times GPT-5.6 Sol's $4/$20 promotional rate, five times GPT-6.1 Sol's $2/$10, and the same premium as Claude Fable 5.1; Fast mode doubles it again. Trails Claude Fable 5.1 on Humanity's Last Exam with tools (57.2% vs 65.0%) and on the Artificial Analysis Intelligence Index (61.2 vs 65.7). OpenAI published no SWE-bench Verified or SWE-bench Pro figure at launch, so it does not appear on our SWE-bench leaderboard. Avoid it if you are cost-sensitive or latency-bound — GPT-6.1 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak. That is the main case for looking at Qwen 3.8 Max instead.
Well behind Claude Fable 5 on SWE-bench Pro (67.7 vs 80.0) and behind several Anthropic models on text rankings. No independent third-party benchmarks at GA — early claims are largely Alibaba-reported. Avoid it if you need independently verified benchmarks or Western data residency. Against GPT-6 Astra specifically, the gap shows up most on coding (99 vs 93).
Take a moderate workload of 10M input and 2M output tokens a month. GPT-6 Astra runs $200.00 (at $10/1M in and $50/1M out); Qwen 3.8 Max runs $32.00 (at $2/1M in and $6/1M out). That is a $168.00/month difference — Qwen 3.8 Max is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is GPT-6 Astra for computer and browser use and long-horizon agentic coding, with Qwen 3.8 Max handling multimodal and vision-heavy workloads at scale. Routing high-volume, low-stakes calls to Qwen 3.8 Max at $2/1M and reserving GPT-6 Astra for the hard cases is usually the cheapest arrangement that does not cost you quality.