Compare / Qwen3.8-Max (0902) vs DeepSeek-V4.1-Flash-1017
Qwen3.8-Max (0902) vs DeepSeek-V4.1-Flash-1017
Which is cheaper, which is more capable, and which does better at each kind of work, from 64 results on 4 sources that measured both models.
AlibabaQwen3.8-Max (0902)
DeepSeekDeepSeek-V4.1-Flash-1017
Qwen3.8-Max (0902) wins 2112 too close to callDeepSeek-V4.1-Flash-1017 wins 31
Of the 64 results both models have, the better value on each. Results whose reported ranges overlap are too close to call.
Results as of 20 October 2026 (catalogue release 2026-10-20-96eb40c3ec63). See also Alibaba vs DeepSeek, every model from both labs.
Qwen3.8-Max (0902) or DeepSeek-V4.1-Flash-1017? The short answer
They score about the same, so pick DeepSeek-V4.1-Flash-1017: it costs 17 times less per product listing on CatalogBench.
- Spring Prompt overall: Qwen3.8-Max (0902) 46, DeepSeek-V4.1-Flash-1017 44 (out of 100; how the score works).
- DeepSeek-V4.1-Flash-1017 is cheaper to run: $0.002 against $0.026 per product listing on CatalogBench (17 times less).
- They score about the same on the Artificial Analysis Intelligence Index (45.4 and 45.1).
- DeepSeek-V4.1-Flash-1017 writes faster: 212 tokens a second against 37.
- DeepSeek-V4.1-Flash-1017 is newer: released 17 Oct 2026, against 3 Aug 2026 for Qwen3.8-Max (0902).
- On our own benchmarks, on the results both models have: Qwen3.8-Max (0902) does better for product listings; DeepSeek-V4.1-Flash-1017 for marketing planning.
- On third-party benchmarks, on the results both models have: Qwen3.8-Max (0902) does better for agents and tool use.
Head to head
46
Spring Prompt overall
44
45.4
Intelligence Index
45.1
$3.00
Price per million tokens, blendedlower is better
$0.52
37
Output speed, tokens a second
212
1,000,000
Context window, tokens
1,048,576
Every fact side by side
| Qwen3.8-Max (0902) | DeepSeek-V4.1-Flash-1017 | |
|---|---|---|
| Price per million tokens | $2.00 in · $6.00 out | $0.30 in · $1.20 out |
| Blended (3 in : 1 out) | $3.00 | $0.52 |
| Cheapest host, blended | – | – |
| Cost per product listing (CatalogBench) | $0.026 | $0.002 |
| Cached input, per million | – | $0.006 cached input |
| Intelligence Index | 45.4 | 45.1 |
| Spring Prompt overall | 46 | 44 |
| Output speed, tokens a second | 37 | 212 |
| Context window | 1,000,000 tokens | 1,048,576 tokens |
| Released | 3 Aug 2026 | 17 Oct 2026 (newer) |
| Weights | Closed | Open |
| Developer based in | China | China |
Prices are the developer's list price where we have read it, otherwise the typical host on OpenRouter; see every model's price. Intelligence and speed from Artificial Analysis.
Which is better for what
Each model's rank among the models on sale today, for each kind of work, from our own benchmarks and licensed sources. Further right is better; the better rank is in green.
Product listingsTurning a sparse product feed and photos into listings that can go live
20 of 24
21 of 24
Decks from an analysisTurning a finished analysis into a deck you could present as it is
13 of 24
12 of 24
User surveysPlanning a user survey and reading its results without being misled
not measured
5 of 13
Marketing planningPlanning a year of ad spend without overspending
19 of 24
11 of 24
Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls
9 of 168
22 of 168
Professional workReal tasks from banking, consulting and law, business documents and freelance projects
18 of 51
not measured
Reasoning and knowledgeHard questions across science, maths and general knowledge
19 of 202
22 of 202
WritingWhat people prefer in blind comparisons, and judged writing quality
17 of 189
not measured
Sticking to the factsSummarising without inventing things, and factual answers
39 of 153
not measured
Speed under pressureGood decisions against a real clock (fast chess)
28 of 28
21 of 27
From our research
Every shared result
Each model's best result on every measure both have, grouped by benchmark, with the setting that produced it. Under each measure, every model that benchmark has measured is a grey tick, best to the right, so you can see where the two sit in the field. The headline measures of each benchmark come first; the rest are under "Show all". Green marks the better value; where a source reports ranges that overlap, neither is marked.
| Measure | Qwen3.8-Max (0902) | DeepSeek-V4.1-Flash-1017 |
|---|---|---|
| CatalogBench measured by Spring Prompt · 3 results | ||
| Cost per productUS dollars, lower is better 18 · 4 of 24 measured | $0.0256 | $0.0015 |
| Reliably publish-ready% of products, higher is better 16 · 19 of 24 measured | 28.6% | 21.4% |
| Reliably publish-ready · sales brief% of products, higher is better 21 · 21 of 24 measured | 0.0% | 0.0% |
| DeckBench measured by Spring Prompt · 3 results | ||
| Cost per deckUS dollars, lower is better 15 · 7 of 24 measured | $0.15 | $0.0255 |
| Deck ratingrating, higher is betterThe rating weighs each win by how strong the other deck was; the win rate counts wins against whichever decks a model happened to meet, so the two can disagree. 13 · 12 of 24 measured | 1,100 | 1,145 |
| Accurate decks% of tasks, higher is better 9 · 14 of 24 measured | 50.0% | 33.3% |
| ROASBench measured by Spring Prompt · 3 results | ||
| Overall scorescore out of 100, higher is better 19 · 11 of 24 measured | 25.4 | 41.4 |
| Audience scorescore out of 100, higher is better 22 · 9 of 24 measured | 39.7 | 65.3 |
| Business scorescore out of 100, higher is better 19 · 11 of 24 measured | 15.6 | 38.3 |
| Artificial Analysis reported by Artificial Analysis · 3 results | ||
| Artificial Analysis Intelligence Indexindex score, higher is better 15 · 16 of 249 measured | 45.4 | 45.1max reasoning |
| Humanity's Last Exam% of questions, higher is better 28 · 27 of 248 measured | 43.1% | 43.3%max reasoning |
| Terminal-Bench 4.0% of tasks, higher is better 13 · 16 of 118 measured | 38.9% | 33.8%max reasoning |
Show all 64 results (52 more)
| Measure | Qwen3.8-Max (0902) | DeepSeek-V4.1-Flash-1017 |
|---|---|---|
| CatalogBench measured by Spring Prompt · 28 results | ||
| Channel compliance% of products, higher is better 1 · 1 of 24 measured | 100.0% | 100.0% |
| Channel rules broken% of products, lower is better 12 · 19 of 24 measured | 0.6% | 1.2% |
| Channel rules broken · sales brief% of products, lower is better 23 · 22 of 24 measured | 3.0% | 2.4% |
| Claims to check% of products, lower is better 1 · 19 of 24 measured | 0.0% | 1.2% |
| Claims to check · sales brief% of products, lower is better 16 · 18 of 24 measured | 1.3% | 3.6% |
| Conflicts caught% of conflicts, higher is better 1 · 1 of 24 measured | 100.0% | 100.0% |
| Content quality% of checks, higher is better 22 · 20 of 24 measured | 94.2% | 95.3% |
| Content quality · sales brief% of checks, higher is better 23 · 19 of 24 measured | 93.6% | 94.8% |
| Decision accuracy% of decisions, higher is better 20 · 18 of 24 measured | 93.3% | 94.9% |
| Failed outputs% of products, lower is better 23 · 1 of 24 measured | 3.6% | 0.0% |
| Failed outputs · sales brief% of products, lower is better 23 · 1 of 24 measured | 3.6% | 0.0% |
| Field accuracy% of missing fields, higher is better 23 · 10 of 24 measured | 90.3% | 93.7% |
| Invented values% of filled values, lower is better 18 · 13 of 24 measured | 2.7% | 1.7% |
| Missing UK information% of products, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| Missing UK information · sales brief% of products, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| Not findable% of products, lower is better 5 · 5 of 24 measured | 3.6% | 3.6% |
| Not findable · sales brief% of products, lower is better 2 · 7 of 24 measured | 1.8% | 2.4% |
| Publish-ready listings% of products, higher is better 15 · 19 of 24 measured | 50.0% | 40.5% |
| Publish-ready listings · sales brief% of products, higher is better 22 · 21 of 24 measured | 7.7% | 8.3% |
| Time per productseconds, lower is better 24 · 3 of 24 measured | 78.4 | 6.70 |
| Unsupported claimsclaims per product, lower is better 13 · 21 of 24 measured | 0.69 | 1.70 |
| Unsupported claims% of products, lower is better 13 · 20 of 24 measured | 26.8% | 44.6% |
| Unsupported claims · sales briefclaims per product, lower is better 23 · 22 of 24 measured | 4.52 | 4.01 |
| Unsupported claims · sales brief% of products, lower is better 22 · 23 of 24 measured | 84.5% | 85.7% |
| Wrong attributes% of products, lower is better 19 · 13 of 24 measured | 21.4% | 17.9% |
| Wrong attributes · sales brief% of products, lower is better 15 · 21 of 24 measured | 19.6% | 22.0% |
| Wrong category or variant% of products, lower is better 5 · 2 of 24 measured | 1.8% | 0.6% |
| Wrong category or variant · sales brief% of products, lower is better 12 · 3 of 24 measured | 2.4% | 1.2% |
| DeckBench measured by Spring Prompt · 14 results | ||
| Caveat dropped% of tasks, lower is better 12 · 12 of 24 measured | 16.7% | 16.7% |
| Clean layout% of tasks, higher is better 6 · 20 of 24 measured | 66.7% | 16.7% |
| Design quality% of the maximum, higher is better 11 · 9 of 24 measured | 67.0% | 68.5% |
| Draft figure quoted% of tasks, lower is better 12 · 19 of 24 measured | 33.3% | 50.0% |
| Findings missing% of tasks, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| Head-to-head win rate% of comparisons, higher is betterThe rating weighs each win by how strong the other deck was; the win rate counts wins against whichever decks a model happened to meet, so the two can disagree. 12 · 14 of 24 measured | 63.5% | 53.3% |
| Layout defects% of tasks, lower is better 6 · 20 of 24 measured | 33.3% | 83.3% |
| Misleading metric used% of tasks, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| Presentable decks% of tasks, higher is better 5 · 12 of 24 measured | 33.3% | 0.0% |
| Recommendation late or wrong% of tasks, lower is better 1 · 9 of 24 measured | 0.0% | 16.7% |
| Slides needing work% of tasks, lower is better 11 · 16 of 24 measured | 50.0% | 66.7% |
| Time per deckseconds, lower is better 24 · 7 of 24 measured | 543 | 73.0 |
| Unsupported claims% of tasks, lower is better 11 · 11 of 24 measured | 16.7% | 16.7% |
| Unsupported numbers% of tasks, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| ROASBench measured by Spring Prompt · 6 results | ||
| Consistency scorescore out of 100, higher is better 16 · 15 of 24 measured | 19.3 | 20.2 |
| Contribution profitsimulated US dollars, higher is better 19 · 10 of 24 measured | $99,775 | $585,572 |
| Cost of a runUS dollars, lower is better 20 · 8 of 24 measured | $0.73 | $0.0799 |
| Months over budgetmonths of 12, lower is better 22 · 1 of 24 measured | 2 | 0 |
| Planning scorescore out of 100, higher is better 11 · 20 of 24 measured | 54.8 | 54.3 |
| Return on ad spendprofit per $1 spent, higher is better 19 · 12 of 24 measured | $0.09 | $0.44 |
| Artificial Analysis reported by Artificial Analysis · 4 results | ||
| Long-context reasoning (AA-LCR)% of questions, higher is better 37 · 19 of 239 measured | 80.3% | 82.7%max reasoning |
| Output speedtokens per second, higher is better 75 · 15 of 78 measured | 36.8 | 212max reasoning |
| SciCode% of problems, higher is better 40 · 24 of 119 measured | 52.1% | 55.9%max reasoning |
| Time to first answer tokenseconds, lower is better 77 · 46 of 78 measured | 56.2 | 10.5max reasoning |
Compare them with other models
- Alibaba vs DeepSeekEvery model from both labs, best against best
- Qwen3.8-Max (0902) vs Claude Opus 5.5Most intelligent at $3–10 per million tokens
- Qwen3.8-Max (0902) vs Claude Haiku 5.5Cheapest with similar intelligence
- Qwen3.8-Max (0902) vs Muse Spark 1.3Fastest with similar intelligence
- DeepSeek-V4.1-Flash-1017 vs Claude Haiku 5.5Cheapest with similar intelligence
- DeepSeek-V4.1-Flash-1017 vs Muse Spark 1.3Fastest with similar intelligence
- DeepSeek-V4.1-Flash-1017 vs Claude Opus 5.5Most intelligent available today
Quick answers
Is Qwen3.8-Max (0902) better than DeepSeek-V4.1-Flash-1017?
They score about the same, so pick DeepSeek-V4.1-Flash-1017: it costs 17 times less per product listing on CatalogBench.
Which is cheaper, Qwen3.8-Max (0902) or DeepSeek-V4.1-Flash-1017?
DeepSeek-V4.1-Flash-1017 is cheaper to run: $0.002 against $0.026 per product listing on CatalogBench (17 times less).
Which is faster, Qwen3.8-Max (0902) or DeepSeek-V4.1-Flash-1017?
DeepSeek-V4.1-Flash-1017 writes about 212 tokens a second on its usual API, against 37 for Qwen3.8-Max (0902) (Artificial Analysis).
Which is better for product listings, Qwen3.8-Max (0902) or DeepSeek-V4.1-Flash-1017?
Qwen3.8-Max (0902) ranks 20 of 24 models on sale for turning a sparse product feed and photos into listings that can go live, against 21 for DeepSeek-V4.1-Flash-1017.
Which is better for decks from an analysis, Qwen3.8-Max (0902) or DeepSeek-V4.1-Flash-1017?
DeepSeek-V4.1-Flash-1017 ranks 12 of 24 models on sale for turning a finished analysis into a deck you could present as it is, against 13 for Qwen3.8-Max (0902).
Which is better for marketing planning, Qwen3.8-Max (0902) or DeepSeek-V4.1-Flash-1017?
DeepSeek-V4.1-Flash-1017 ranks 11 of 24 models on sale for planning a year of ad spend without overspending, against 19 for Qwen3.8-Max (0902).
Which is better for agents and tool use, Qwen3.8-Max (0902) or DeepSeek-V4.1-Flash-1017?
Qwen3.8-Max (0902) ranks 9 of 168 models on sale for multi-step tasks with tools: support desks, coding agents, function calls, against 22 for DeepSeek-V4.1-Flash-1017.
Which has the bigger context window, Qwen3.8-Max (0902) or DeepSeek-V4.1-Flash-1017?
DeepSeek-V4.1-Flash-1017: 1,048,576 tokens, against 1,000,000 for Qwen3.8-Max (0902).
Run Qwen3.8-Max (0902) and DeepSeek-V4.1-Flash-1017 on your own prompt
Benchmarks aren't your data. Try both side by side in the playground, with the cost of every answer.