Compare / Claude Opus 5.5 vs DeepSeek-V4.1-Flash-1017
Claude Opus 5.5 vs DeepSeek-V4.1-Flash-1017
Which is cheaper, which is more capable, and which does better at each kind of work, from 84 results on 5 sources that measured both models.
AnthropicClaude Opus 5.5
DeepSeekDeepSeek-V4.1-Flash-1017
Claude Opus 5.5 wins 5617 too close to callDeepSeek-V4.1-Flash-1017 wins 11
Of the 84 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 Anthropic vs DeepSeek, every model from both labs.
Claude Opus 5.5 or DeepSeek-V4.1-Flash-1017? The short answer
Pick Claude Opus 5.5 if quality matters most: it scores higher (Spring Prompt overall 85 against 44). Pick DeepSeek-V4.1-Flash-1017 if cost matters more: it costs 26 times less per product listing on CatalogBench. DeepSeek-V4.1-Flash-1017 is also faster.
- Spring Prompt overall: Claude Opus 5.5 85, 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 $0.040 per product listing on CatalogBench (26 times less).
- Claude Opus 5.5 scores higher on the Artificial Analysis Intelligence Index: 57.6 against 45.1.
- DeepSeek-V4.1-Flash-1017 writes faster: 212 tokens a second against 96.
- DeepSeek-V4.1-Flash-1017 is newer: released 17 Oct 2026, against 22 Sep 2026 for Claude Opus 5.5.
- On our own benchmarks, on the results both models have: Claude Opus 5.5 does better for product listings, decks from an analysis, user surveys and marketing planning.
- On third-party benchmarks, on the results both models have: Claude Opus 5.5 does better for agents and tool use and reasoning and knowledge.
Head to head
85
Spring Prompt overall
44
57.6
Intelligence Index
45.1
$8.00
Price per million tokens, blendedlower is better
$0.52
96
Output speed, tokens a second
212
1,000,000
Context window, tokens
1,048,576
Every fact side by side
| Claude Opus 5.5 | DeepSeek-V4.1-Flash-1017 | |
|---|---|---|
| Price per million tokens | $4.00 in · $20.00 out | $0.30 in · $1.20 out |
| Blended (3 in : 1 out) | $8.00 | $0.52 |
| Cheapest host, blended | $8.00 (Amazon Bedrock) | – |
| Cost per product listing (CatalogBench) | $0.040 | $0.002 |
| Cached input, per million | $0.20 cached input | $0.006 cached input |
| Intelligence Index | 57.6 | 45.1 |
| Spring Prompt overall | 85 | 44 |
| Output speed, tokens a second | 96 | 212 |
| Context window | 1,000,000 tokens | 1,048,576 tokens |
| Released | 22 Sep 2026 | 17 Oct 2026 (newer) |
| Weights | Closed | Open |
| Developer based in | the US | 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
9 of 24
21 of 24
Decks from an analysisTurning a finished analysis into a deck you could present as it is
4 of 24
12 of 24
User surveysPlanning a user survey and reading its results without being misled
1 of 13
5 of 13
Marketing planningPlanning a year of ad spend without overspending
5 of 24
11 of 24
Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls
2 of 168
22 of 168
Professional workReal tasks from banking, consulting and law, business documents and freelance projects
3 of 51
not measured
Reasoning and knowledgeHard questions across science, maths and general knowledge
1 of 202
22 of 202
WritingWhat people prefer in blind comparisons, and judged writing quality
5 of 189
not measured
Sticking to the factsSummarising without inventing things, and factual answers
3 of 153
not measured
Speed under pressureGood decisions against a real clock (fast chess)
28 of 28
21 of 27
From our research
- AI Checkpoint: Argon matches Opus, until it sells 16 Oct 2026
- Gemini 4 Argon review: Opus-level product data at a quarter of the cost, until you ask it to sell 14 Oct 2026
- Claude Opus 5.5 vs Muse Wonder Full: Eight Crafts, and Opus Still Wins Most 13 Oct 2026
- Muse Wonder review: good analysis, work that still needs checking 13 Oct 2026
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 | Claude Opus 5.5 | DeepSeek-V4.1-Flash-1017 |
|---|---|---|
| CatalogBench measured by Spring Prompt · 3 results | ||
| Cost per productUS dollars, lower is better 22 · 4 of 24 measured | $0.0400 | $0.0015 |
| Reliably publish-ready% of products, higher is better 7 · 19 of 24 measured | 55.4% | 21.4% |
| Reliably publish-ready · sales brief% of products, higher is better 9 · 21 of 24 measured | 21.4% | 0.0% |
| DeckBench measured by Spring Prompt · 3 results | ||
| Cost per deckUS dollars, lower is better 21 · 7 of 24 measured | $0.26 | $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. 4 · 12 of 24 measured | 1,436 | 1,145 |
| Accurate decks% of tasks, higher is better 4 · 14 of 24 measured | 83.3% | 33.3% |
| ROASBench measured by Spring Prompt · 3 results | ||
| Overall scorescore out of 100, higher is better 5 · 11 of 24 measured | 51.7 | 41.4 |
| Audience scorescore out of 100, higher is better 4 · 9 of 24 measured | 67.7 | 65.3 |
| Business scorescore out of 100, higher is better 5 · 11 of 24 measured | 52.3 | 38.3 |
| SurveyBench measured by Spring Prompt · 3 results | ||
| Analysis ratingrating, higher is betterThe rating weighs each win by how strong the other analysis was; the win rate counts wins against whichever analyses a model happened to meet, so the two can disagree. 1 · 5 of 13 measured | 1,499 | 1,221 |
| Cost per taskUS dollars, lower is better 12 · 4 of 12 measured | $0.46 | $0.0373 |
| Analysis scorepoints of 100, higher is better 3 · 4 of 13 measured | 95.3 | 90.2 |
| Artificial Analysis reported by Artificial Analysis · 3 results | ||
| Artificial Analysis Intelligence Indexindex score, higher is better 1 · 16 of 249 measured | 57.6max reasoning | 45.1max reasoning |
| Humanity's Last Exam% of questions, higher is better 1 · 27 of 248 measured | 61.4%max reasoning | 43.3%max reasoning |
| Terminal-Bench 4.0% of tasks, higher is better 2 · 16 of 118 measured | 59.6%max reasoning | 33.8%max reasoning |
Show all 84 results (69 more)
| Measure | Claude Opus 5.5 | 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 1 · 19 of 24 measured | 0.0% | 1.2% |
| Channel rules broken · sales brief% of products, lower is better 12 · 22 of 24 measured | 0.6% | 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 1 · 18 of 24 measured | 0.0% | 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 5 · 20 of 24 measured | 97.9% | 95.3% |
| Content quality · sales brief% of checks, higher is better 8 · 19 of 24 measured | 97.8% | 94.8% |
| Decision accuracy% of decisions, higher is better 6 · 18 of 24 measured | 97.7% | 94.9% |
| Failed outputs% of products, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| Failed outputs · sales brief% of products, lower is better 1 · 1 of 24 measured | 0.0% | 0.0% |
| Field accuracy% of missing fields, higher is better 18 · 10 of 24 measured | 92.2% | 93.7% |
| Invented values% of filled values, lower is better 4 · 13 of 24 measured | 0.5% | 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 4 · 5 of 24 measured | 3.0% | 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 5 · 19 of 24 measured | 70.8% | 40.5% |
| Publish-ready listings · sales brief% of products, higher is better 10 · 21 of 24 measured | 34.5% | 8.3% |
| Time per productseconds, lower is better 14 · 3 of 24 measured | 16.9 | 6.70 |
| Unsupported claimsclaims per product, lower is better 9 · 21 of 24 measured | 0.45 | 1.70 |
| Unsupported claims% of products, lower is better 12 · 20 of 24 measured | 17.3% | 44.6% |
| Unsupported claims · sales briefclaims per product, lower is better 11 · 22 of 24 measured | 1.66 | 4.01 |
| Unsupported claims · sales brief% of products, lower is better 12 · 23 of 24 measured | 60.1% | 85.7% |
| Wrong attributes% of products, lower is better 6 · 13 of 24 measured | 15.5% | 17.9% |
| Wrong attributes · sales brief% of products, lower is better 7 · 21 of 24 measured | 15.5% | 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 5 · 3 of 24 measured | 1.8% | 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 3 · 20 of 24 measured | 83.3% | 16.7% |
| Design quality% of the maximum, higher is better 7 · 9 of 24 measured | 72.0% | 68.5% |
| Draft figure quoted% of tasks, lower is better 1 · 19 of 24 measured | 0.0% | 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. 3 · 14 of 24 measured | 87.8% | 53.3% |
| Layout defects% of tasks, lower is better 3 · 20 of 24 measured | 16.7% | 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 16 · 16 of 24 measured | 66.7% | 66.7% |
| Time per deckseconds, lower is better 13 · 7 of 24 measured | 98.5 | 73.0 |
| Unsupported claims% of tasks, lower is better 1 · 11 of 24 measured | 0.0% | 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 7 · 15 of 24 measured | 33.8 | 20.2 |
| Contribution profitsimulated US dollars, higher is better 2 · 10 of 24 measured | $911,566 | $585,572 |
| Cost of a runUS dollars, lower is better 22 · 8 of 24 measured | $1.06 | $0.0799 |
| Months over budgetmonths of 12, lower is better 1 · 1 of 24 measured | 0 | 0 |
| Planning scorescore out of 100, higher is better 16 · 20 of 24 measured | 54.7 | 54.3 |
| Return on ad spendprofit per $1 spent, higher is better 2 · 12 of 24 measured | $0.69 | $0.44 |
| SurveyBench measured by Spring Prompt · 17 results | ||
| Head-to-head win rate% of comparisons, higher is betterThe rating weighs each win by how strong the other analysis was; the win rate counts wins against whichever analyses a model happened to meet, so the two can disagree. 1 · 5 of 13 measured | 83.7% | 60.2% |
| Leading or double-barrelled questions% of tasks, lower is better 1 · 3 of 13 measured | 0.0% | 11.1% |
| Mistaken next steps% of tasks, lower is better 1 · 1 of 13 measured | 0.0% | 0.0% |
| Numbers right% of answers, higher is better 1 · 1 of 13 measured | 100.0% | 100.0% |
| Objectives unanswerable% of tasks, lower is better 1 · 7 of 13 measured | 0.0% | 11.1% |
| Precision ignored% of tasks, lower is better 1 · 1 of 13 measured | 0.0% | 0.0% |
| Research scorepoints of 100, higher is better 2 · 4 of 13 measured | 96.1 | 90.9 |
| Sound research% of tasks, higher is better 2 · 3 of 13 measured | 55.6% | 33.3% |
| Stakeholder's view presumed% of tasks, lower is better 1 · 1 of 13 measured | 0.0% | 0.0% |
| Survey plan scorepoints of 100, higher is better 1 · 4 of 13 measured | 97.8 | 92.5 |
| Survey structure% of tasks, lower is better 3 · 10 of 13 measured | 11.1% | 33.3% |
| Time per taskseconds, lower is better 9 · 6 of 12 measured | 180 | 134 |
| Traps handled% of traps, higher is better 2 · 5 of 13 measured | 95.7% | 91.3% |
| Traps missed% of tasks, lower is better 3 · 5 of 13 measured | 33.3% | 44.4% |
| Unsupported findings% of tasks, lower is better 3 · 4 of 13 measured | 22.2% | 44.4% |
| Wrong numbers% of tasks, lower is better 1 · 1 of 13 measured | 0.0% | 0.0% |
| Wrong recommendation% of tasks, lower is better 1 · 3 of 13 measured | 0.0% | 11.1% |
| Artificial Analysis reported by Artificial Analysis · 4 results | ||
| Long-context reasoning (AA-LCR)% of questions, higher is better 4 · 19 of 239 measured | 84.7%max reasoning | 82.7%max reasoning |
| Output speedtokens per second, higher is better 46 · 15 of 78 measured | 96.0max reasoning | 212max reasoning |
| SciCode% of problems, higher is better 1 · 24 of 119 measured | 66.9%max reasoning | 55.9%max reasoning |
| Time to first answer tokenseconds, lower is better 43 · 46 of 78 measured | 6.89low reasoning | 10.5max reasoning |
Compare them with other models
- Anthropic vs DeepSeekEvery model from both labs, best against best
- Claude Opus 5.5 vs GPT-6.1 SolJust below overall
- Claude Opus 5.5 vs Claude Fable 5.1Anthropic's best other model
- 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
Quick answers
Is Claude Opus 5.5 better than DeepSeek-V4.1-Flash-1017?
Pick Claude Opus 5.5 if quality matters most: it scores higher (Spring Prompt overall 85 against 44). Pick DeepSeek-V4.1-Flash-1017 if cost matters more: it costs 26 times less per product listing on CatalogBench. DeepSeek-V4.1-Flash-1017 is also faster.
Which is cheaper, Claude Opus 5.5 or DeepSeek-V4.1-Flash-1017?
DeepSeek-V4.1-Flash-1017 is cheaper to run: $0.002 against $0.040 per product listing on CatalogBench (26 times less).
Which is faster, Claude Opus 5.5 or DeepSeek-V4.1-Flash-1017?
DeepSeek-V4.1-Flash-1017 writes about 212 tokens a second on its usual API, against 96 for Claude Opus 5.5 (Artificial Analysis).
Which is better for product listings, Claude Opus 5.5 or DeepSeek-V4.1-Flash-1017?
Claude Opus 5.5 ranks 9 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, Claude Opus 5.5 or DeepSeek-V4.1-Flash-1017?
Claude Opus 5.5 ranks 4 of 24 models on sale for turning a finished analysis into a deck you could present as it is, against 12 for DeepSeek-V4.1-Flash-1017.
Which is better for user surveys, Claude Opus 5.5 or DeepSeek-V4.1-Flash-1017?
Claude Opus 5.5 ranks 1 of 13 models on sale for planning a user survey and reading its results without being misled, against 5 for DeepSeek-V4.1-Flash-1017.
Which is better for marketing planning, Claude Opus 5.5 or DeepSeek-V4.1-Flash-1017?
Claude Opus 5.5 ranks 5 of 24 models on sale for planning a year of ad spend without overspending, against 11 for DeepSeek-V4.1-Flash-1017.
Which has the bigger context window, Claude Opus 5.5 or DeepSeek-V4.1-Flash-1017?
DeepSeek-V4.1-Flash-1017: 1,048,576 tokens, against 1,000,000 for Claude Opus 5.5.
Run Claude Opus 5.5 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.