{"id":931,"date":"2026-09-18T07:22:22","date_gmt":"2026-09-18T07:22:22","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=931"},"modified":"2026-09-18T07:22:24","modified_gmt":"2026-09-18T07:22:24","slug":"qwen-3-8-max-0902-vs-gpt-6-astra","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/qwen-3-8-max-0902-vs-gpt-6-astra","title":{"rendered":"Qwen3.8 Max 0902 vs GPT-6 Astra: Benchmarks, Price & Which to Pick (2026)"},"content":{"rendered":"\n\n\n
\n\n

Comparison \u00b7 September 2026<\/p>\n\n

\nQwen3.8 Max 0902 (Alibaba API) and GPT-6 Astra (OpenAI API) are two models teams actually route in 2026. This is a decision guide, not a leaderboard dump: published API rates, three workload cost scenarios, live writing and coding snippets, and a pick matrix you can rerun. If you want both without juggling tabs, use a\nmulti-model AI workspace<\/a>\nor start on\ni10X<\/a>.\n<\/p>\n\n

\nQuick verdict<\/strong>\n

Pick Qwen3.8 Max 0902 if:<\/strong> you want Alibaba Qwen Max quality near GPT-class tasks at $2 \/ $6, video-capable input, ~1M context, and far lower workload cost than Astra.<\/p>\n

Pick GPT-6 Astra if:<\/strong> you want OpenAI’s GPT-6 flagship card for the hardest end-to-end research and engineering narratives, file-first intake, and you can pay $10 \/ $50.<\/p>\n

Best default for many teams:<\/strong> route by task and keep both available. See the decision matrix below.<\/p>\n

Data checked: 2026-09-07. Prices and model cards change. Verify live API test and vendor pages.<\/em><\/p>\n<\/div>\n\n

\n\n\n\n\n\n\n

1M<\/strong><\/p><\/td>\n

Qwen3.8 Max 0902 context (API)<\/p><\/td>\n<\/tr>\n

1.05M<\/strong><\/p><\/td>\n

GPT-6 Astra context (API)<\/p><\/td>\n<\/tr>\n

$2 \/ $6<\/strong><\/p><\/td>\n

Qwen3.8 Max 0902 input\/output per 1M tokens (API pricing, 2026-09-07)<\/p><\/td>\n<\/tr>\n

$10 \/ $50<\/strong><\/p><\/td>\n

GPT-6 Astra input\/output per 1M tokens (API pricing, 2026-09-07)<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n

\n\"Bar\n
Figure 1.<\/strong> Where each model wins on relative axes (context, output cost efficiency, writing usefulness, coding micro-test, cache-read price). Higher is stronger for that axis. Chart: i10X.<\/figcaption>\n<\/figure>\n\n
\n

Persona picker<\/h2>\n\n\n\n\n\n\n\n

You are…<\/p><\/th>\n

Start with<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Writer \/ marketer<\/p><\/td>\n

Either (23\/25 both); Qwen for warmer named tone<\/p><\/td>\n

Qwen wrote a warm named follow-up under 120 words. Astra was shorter. Same score.<\/p><\/td>\n<\/tr>\n

Developer \/ agent builder<\/p><\/td>\n

Either for small bugs; Astra for flagship positioning; Qwen for cost<\/p><\/td>\n

Micro-test tied at 24\/25. Qwen offered return 0 or ValueError. Astra preferred ValueError.<\/p><\/td>\n<\/tr>\n

Researcher \/ analyst<\/p><\/td>\n

Split on window; Astra for flagship research card<\/p><\/td>\n

1M Qwen vs 1.05M Astra. Nearly matched windows. Card story differs.<\/p><\/td>\n<\/tr>\n

Budget \/ high volume<\/p><\/td>\n

Qwen3.8 Max 0902<\/p><\/td>\n

Large gap on every workload estimate despite both being ‘max\/flagship’ adjacent tiers.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\n\n

What we are comparing (exact versions)<\/h2>\n

This page compares two specific API models, not vague brand labels. Sibling GPT-6 Astra pairs live in the related list below. For routing across many models, see\nAI model routing<\/a>.<\/p>\n\n\n\n\n\n\n\n\n

Field<\/p><\/th>\n

Qwen3.8 Max 0902<\/p><\/th>\n

GPT-6 Astra<\/p><\/th>\n<\/tr>\n

Provider<\/p><\/td>\n

Alibaba (Qwen)<\/p><\/td>\n

OpenAI<\/p><\/td>\n<\/tr>\n

API model<\/p><\/td>\n

Qwen3.8 Max 0902 (Alibaba API)<\/code><\/p><\/td>\n

GPT-6 Astra (OpenAI API)<\/code><\/p><\/td>\n<\/tr>\n

Listed card name<\/p><\/td>\n

Qwen: Qwen3.8 Max (0902)<\/p><\/td>\n

OpenAI: GPT-6 Astra<\/p><\/td>\n<\/tr>\n

Family \/ tier<\/p><\/td>\n

Alibaba Qwen3.8 Max snapshot (0902)<\/p><\/td>\n

OpenAI GPT-6 flagship (Astra)<\/p><\/td>\n<\/tr>\n

App vs API note<\/p><\/td>\n

Qwen \/ Alibaba Cloud product surfaces may differ; this article uses the API model above<\/p><\/td>\n

Also in ChatGPT-family apps; this article uses the API model above<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

If a page still compares older IDs as if they were these models, treat it as historical.<\/p>\n\n

\n\n

Spec sheet (API card, 2026-09-07)<\/h2>\n\n\n\n\n\n\n\n\n

Spec<\/p><\/th>\n

Qwen3.8 Max 0902<\/p><\/th>\n

GPT-6 Astra<\/p><\/th>\n<\/tr>\n

Context window<\/p><\/td>\n

1,000,000 tokens<\/p><\/td>\n

1,050,000 tokens<\/p><\/td>\n<\/tr>\n

Input modalities (card)<\/p><\/td>\n

text, image, video<\/p><\/td>\n

file, image, text<\/p><\/td>\n<\/tr>\n

Output<\/p><\/td>\n

text<\/p><\/td>\n

text<\/p><\/td>\n<\/tr>\n

Open weights<\/p><\/td>\n

No<\/p><\/td>\n

No<\/p><\/td>\n<\/tr>\n

Vendor positioning (card)<\/p><\/td>\n

Updated Qwen3.8 Max snapshot; large mixture-of-experts model accepting text, image, and video and returning text<\/p><\/td>\n

OpenAI flagship for demanding end-to-end work: advanced analysis, software engineering, deep research, scientific work, and document creation, with long-horizon strengths<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

We did not invent max-output, tok\/s, or leaderboard rows. Those fields were not used for this pair. If your product depends on a specific tool or effort switch, confirm it on the live vendor page before you ship.<\/p>\n\n

\n\n

Pricing and real workload cost<\/h2>\n

List prices are easy to game. Workload cost is what you feel. Numbers below use published per-million rates as of 2026-09-07<\/strong>. Verify live<\/strong> before you budget.<\/p>\n\n\n\n\n\n\n

Price<\/p><\/th>\n

Qwen3.8 Max 0902<\/p><\/th>\n

GPT-6 Astra<\/p><\/th>\n<\/tr>\n

Input \/ 1M tokens<\/p><\/td>\n

$2<\/p><\/td>\n

$10<\/p><\/td>\n<\/tr>\n

Output \/ 1M tokens<\/p><\/td>\n

$6<\/p><\/td>\n

$50<\/p><\/td>\n<\/tr>\n

Cache read \/ 1M<\/p><\/td>\n

$0.25<\/p><\/td>\n

$1<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Matched-ish context, very different stickers. Qwen $2 \/ $6 vs Astra $10 \/ $50.<\/p>\n\n\n\n\n\n\n

Scenario<\/p><\/th>\n

Assumed tokens<\/p><\/th>\n

Est. Qwen3.8 Max 0902<\/p><\/th>\n

Est. GPT-6 Astra<\/p><\/th>\n<\/tr>\n

Chat turn<\/p><\/td>\n

1k in + 0.5k out<\/p><\/td>\n

$0.005<\/p><\/td>\n

$0.035<\/p><\/td>\n<\/tr>\n

Repo \/ doc review<\/p><\/td>\n

80k in + 4k out<\/p><\/td>\n

$0.184<\/p><\/td>\n

$1<\/p><\/td>\n<\/tr>\n

Agent loop<\/p><\/td>\n

200k in (50% cached) + 20k out<\/p><\/td>\n

$0.345<\/p><\/td>\n

$2.1<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n\"Bar\n
Figure 2.<\/strong> Estimated USD per run using published API list rates (2026-09-07). Chat is 1k in + 0.5k out. Repo review is 80k in + 4k out. Agent loop is 200k in with 50% cache hits + 20k out. Chart: i10X.<\/figcaption>\n<\/figure>\n

Qwen wins the cached agent loop (~$0.345 vs $2.10). Astra’s cheaper relative cache vs its own input still cannot close the gap in this recipe. For subscription stacks, see\nAI subscription stack cost<\/a>.<\/p>\n\n

\n\n

Performance by job (not one score)<\/h2>\n

Public benches disagree by harness, effort mode, and date. We are not inventing index numbers we do not have for this pair. Treat vendor cards as positioning, treat our snippets as a small live pack, and prefer your own side-by-side on your prompts. Method:\nside-by-side AI comparison<\/a>.<\/p>\n\n

Coding and agents<\/h3>\n

Both scored 24\/25. Both named ZeroDivisionError. Qwen’s minimal fix returned 0 by default with a ValueError option. Astra raised ValueError and mentioned None. Pick the contract your callers expect.<\/p>\n\n

Writing and tone<\/h3>\n

Tie at 23\/25. Qwen: named warm letter with Wednesday ask and Acme line. Astra: shorter follow-up, same facts. Brand voice decides.<\/p>\n\n

Research, math, reasoning<\/h3>\n

Both refused the cheese Moon. Qwen stopped at rock\/regolith and offered real lunar resource talk instead. Astra redirected to bioreactor protein. Windows nearly match (1M vs 1.05M). For publishable work, add\nmulti-model hallucination checks<\/a>.<\/p>\n\n

Multimodal and long context<\/h3>\n

Qwen: text, image, video. Astra: file, image, text. Video vs file is the modality fork. Context is close.<\/p>\n\n

Speed<\/h3>\n

No tok\/s in this pack. Measure p50 from your API region. Do not ship on a third-party screenshot.<\/p>\n\n\n\n\n\n\n\n\n\n

Job<\/p><\/th>\n

Edge<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Hard coding \/ agents<\/p><\/td>\n

Split<\/p><\/td>\n

Tie on micro-test. Qwen cheaper for volume. Astra flagship card for hard SE narratives.<\/p><\/td>\n<\/tr>\n

Everyday writing<\/p><\/td>\n

Split<\/p><\/td>\n

Same score. Qwen warmer named. Astra tighter.<\/p><\/td>\n<\/tr>\n

Long docs \/ multimodal<\/p><\/td>\n

Split<\/p><\/td>\n

1M vs 1.05M. Qwen for video packs. Astra for file packs.<\/p><\/td>\n<\/tr>\n

Realtime \/ conversational<\/p><\/td>\n

Product-dependent<\/p><\/td>\n

Not measured.<\/p><\/td>\n<\/tr>\n

Cost at volume<\/p><\/td>\n

Qwen3.8 Max 0902<\/p><\/td>\n

Chat ~$0.005 vs $0.035. Repo ~$0.184 vs $1.00. Agent ~$0.345 vs $2.10.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\nHow to read benchmarks<\/strong>\n

Leaderboards mix harnesses, tool settings, and effort modes. When we do not have a primary table for a pair, we do not invent one. Re-test on your workload.<\/p>\n<\/div>\n\n

\n\n

Side-by-side test (i10X pack, 2026-09-07)<\/h2>\n

Same three prompts, scored 1-5 on instruction following, depth, factual caution, style, and usefulness (max 25). Writing, coding, and false premise only. No invented extra scores. Pack tied 69\/75.<\/p>\n\n

Test 1: Client email rewrite<\/h3>\n

Task:<\/strong> Keep every fact. Warmer. Under 120 words.<\/p>\n

Qwen3.8 Max 0902 (excerpt, sanitized):<\/strong> Hi [Name], warm week line, Q3 deck Tuesday, finance still missing, move stakeholders to next Wednesday, Acme pricing on competitive slide, thanks.<\/p>\n

GPT-6 Astra (excerpt, sanitized):<\/strong> Short follow-up with the same operational facts and a Wednesday ask.<\/p>\n

Edge:<\/strong> Tie on score. Qwen for warmth + name. Astra for compression.<\/p>\n\n

Test 2: Empty-list average bug<\/h3>\n

Tie 24\/25. Empty-list guard both ways. Default empty behavior differs (0 vs raise).<\/p>\n\n

Test 3: False premise (Moon cheese)<\/h3>\n

Both pass. Qwen: short refuse, offer real lunar resources. Astra: refuse plus bioreactor plan. 22\/25 each.<\/p>\n\n\n\n\n\n\n\n\n

Prompt type<\/p><\/th>\n

Qwen3.8 Max 0902<\/p><\/th>\n

GPT-6 Astra<\/p><\/th>\n

Note<\/p><\/th>\n<\/tr>\n

Client email rewrite<\/p><\/td>\n

23\/25<\/p><\/td>\n

23\/25<\/p><\/td>\n

Tie on score. Qwen for warmth + name. Astra for compression.<\/p><\/td>\n<\/tr>\n

Bug explain + minimal fix<\/p><\/td>\n

24\/25<\/p><\/td>\n

24\/25<\/p><\/td>\n

Tie 24\/25. Empty-list guard both ways. Default empty behavior differs (0 vs raise).<\/p><\/td>\n<\/tr>\n

Logic + false premise<\/p><\/td>\n

22\/25<\/p><\/td>\n

22\/25<\/p><\/td>\n

Both pass. Qwen: short refuse, offer real lunar resources. Astra: refuse plus bioreactor plan. 22\/25 each.<\/p><\/td>\n<\/tr>\n

Total<\/strong><\/p><\/td>\n

69\/75<\/strong><\/p><\/td>\n

69\/75<\/strong><\/p><\/td>\n

Jobs still split on cost, context, and vendor fit.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

A writing or coding edge does not erase a cost or context win. Route the next step.<\/p>\n\n

\n\n

Ecosystem and where you run them<\/h2>\n