DeepSeek V3.1 Terminus vs Qwen3.5-Flash
Qwen3.5-Flash costs 3.9x less per output token than DeepSeek V3.1 Terminus ($0.26 vs $1.0142 per million). Updated October 22, 2026; 23 benchmarks have a result for both.
DeepSeek V3.1 Terminus or Qwen3.5-Flash: which to pick
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; DeepSeek V3.1 Terminus has none, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 Terminus for work measured by ALE-Bench or LMArena Text: coding, where it leads.
- Shared results
- 23
- DeepSeek V3.1 Terminus only
- 22
- Qwen3.5-Flash only
- 32
- Like-for-like categories
- 5 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Qwen3.5-Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only DeepSeek V3.1 Terminus has a AA Intelligence Index result, 14.8.
Directional only
Agentic work
AA Agentic Index
No call
Only DeepSeek V3.1 Terminus has a AA Agentic Index result, 6.8.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.5-Flash has a GPQA Diamond result, 82.8%.
Directional only
Math
AIME
No call
Only Qwen3.5-Flash has a AIME result, 92.5%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.5-Flash has a MMLU Pro result, 84.1%.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.1 Terminus has a Design Arena: websites result, 1187.
Directional only
Long documents
Listed context window
Qwen3.5-Flash
Qwen3.5-Flash takes 1M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
DeepSeek V3.1 Terminus $0.0008, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.5-Flash has the number, $0.40.
Directional only
At a glance
- Coding index
- DeepSeek V3.1 Terminus not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- Knowledge, 7.4 points apart on average over 4 shared benchmarks.
- Widest single gap
- ALE-Bench: DeepSeek V3.1 Terminus 745.2 against Qwen3.5-Flash 265.9, 16.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 Terminus $1.0142 against Qwen3.5-Flash $0.26 per million output tokens, 3.9x apart.
- Context window
- DeepSeek V3.1 Terminus takes 164K tokens, Qwen3.5-Flash 1M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V3.1 Terminus | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 0 | 1 | ||
| Knowledge | 4 | 0 | ||
| General | 11 | 2 | ||
| All shared boards | 20 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | DeepSeek V3.1 Terminus | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1244 | Only Qwen3.5-Flash measured |
| AA Coding Index | 43.5 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LiveCodeBench (Vals AI) | Not measured | 83.3% | Only Qwen3.5-Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 64.4% | Only Qwen3.5-Flash measured |
| LiveCodeBench (Artificial Analysis run) | 79.8% | Not measured | Only DeepSeek V3.1 Terminus measured |
| SciCode | 38.0% | Not measured | Only DeepSeek V3.1 Terminus measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | Not measured | Only DeepSeek V3.1 Terminus measured |
| ALE-Bench | 745.2 | 265.9 | DeepSeek V3.1 Terminus |
| LMArena Text: coding | 1425 | 1412 | DeepSeek V3.1 Terminus |
| LMArena Text: software and IT | 1426 | 1416 | DeepSeek V3.1 Terminus |
| LMArena WebDev: React | Not measured | 1229 | Only Qwen3.5-Flash measured |
| LMArena WebDev: HTML | Not measured | 1291 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.8 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Epoch Capabilities Index | Not measured | 143.5 | Only Qwen3.5-Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 6.8 | Not measured | Only DeepSeek V3.1 Terminus measured |
| τ²-Bench Telecom | 37.1% | Not measured | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.1 Terminus | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.8% | Only Qwen3.5-Flash measured |
| MMMU Pro (Vals AI) | Not measured | 81.9% | Only Qwen3.5-Flash measured |
| GPQA Diamond (Artificial Analysis run) | 79.2% | Not measured | Only DeepSeek V3.1 Terminus measured |
| Humanity's Last Exam (Artificial Analysis run) | 16.4% | Not measured | Only DeepSeek V3.1 Terminus measured |
| GPQA Diamond (Epoch AI run) | Not measured | 82.3% | Only Qwen3.5-Flash measured |
| Chess puzzles | Not measured | 14.0% | Only Qwen3.5-Flash measured |
| Mystery game puzzles | Not measured | 20.0% | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts | 1426 | 1403 | DeepSeek V3.1 Terminus |
| LMArena Text: expert | Not measured | 1407 | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts in English | 1432 | 1409 | DeepSeek V3.1 Terminus |
| Kagi LLM Benchmark | 57.4% | Not measured | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 Terminus | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| AA Math Index | 89.7 | Not measured | Only DeepSeek V3.1 Terminus measured |
| AIME 2025 (Artificial Analysis run) | 89.7% | Not measured | Only DeepSeek V3.1 Terminus measured |
| OTIS Mock AIME | Not measured | 84.4% | Only Qwen3.5-Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 18.2% | Only Qwen3.5-Flash measured |
| LMArena Text: math | 1402 | 1407 | Qwen3.5-Flash |
| LMArena Text: mathematical work | Not measured | 1413 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.1 Terminus | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.1% | Only Qwen3.5-Flash measured |
| LegalBench (Vals AI) | Not measured | 84.3% | Only Qwen3.5-Flash measured |
| CaseLaw (Vals AI) | Not measured | 56.0% | Only Qwen3.5-Flash measured |
| CorpFin (Vals AI) | Not measured | 63.6% | Only Qwen3.5-Flash measured |
| TaxEval (Vals AI) | Not measured | 72.2% | Only Qwen3.5-Flash measured |
| MortgageTax (Vals AI) | Not measured | 67.4% | Only Qwen3.5-Flash measured |
| MedCode (Vals AI) | Not measured | 33.0% | Only Qwen3.5-Flash measured |
| MMLU Pro (Artificial Analysis run) | 85.1% | Not measured | Only DeepSeek V3.1 Terminus measured |
| SimpleQA Verified | Not measured | 20.3% | Only Qwen3.5-Flash measured |
| LMArena Text: science | 1451 | 1415 | DeepSeek V3.1 Terminus |
| LMArena Text: medicine and healthcare | 1468 | 1407 | DeepSeek V3.1 Terminus |
| LMArena Text: legal and government | 1457 | 1398 | DeepSeek V3.1 Terminus |
| LMArena Text: business and finance | 1414 | 1394 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1187 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: UI components | 1180 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: game dev | 1131 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: data viz | 1168 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: 3D | 1141 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: all code categories | 1175 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: SVG | 1065 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index112
| Benchmark | DeepSeek V3.1 Terminus | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 42.5% | Only Qwen3.5-Flash measured |
| MedScribe (Vals AI) | Not measured | 70.6% | Only Qwen3.5-Flash measured |
| IFBench | 57.0% | Not measured | Only DeepSeek V3.1 Terminus measured |
| Long context reasoning (AA-LCR) | 69.3% | Not measured | Only DeepSeek V3.1 Terminus measured |
| DTBench | 81.3% | 82.9% | Qwen3.5-Flash |
| LMCA | 28.6% | 29.1% | Qwen3.5-Flash |
| LMArena Text | 1419 | 1397 | DeepSeek V3.1 Terminus |
| LMArena Text: instruction following | 1404 | 1374 | DeepSeek V3.1 Terminus |
| LMArena Text: creative writing | 1403 | 1343 | DeepSeek V3.1 Terminus |
| LMArena Text: English | 1429 | 1404 | DeepSeek V3.1 Terminus |
| LMArena Text: non-English | 1408 | 1385 | DeepSeek V3.1 Terminus |
| LMArena Text: Chinese | Not measured | 1445 | Only Qwen3.5-Flash measured |
| LMArena Text: German | Not measured | 1390 | Only Qwen3.5-Flash measured |
| LMArena Text: Japanese | Not measured | 1369 | Only Qwen3.5-Flash measured |
| LMArena Text: Korean | Not measured | 1344 | Only Qwen3.5-Flash measured |
| LMArena Text: French | Not measured | 1413 | Only Qwen3.5-Flash measured |
| LMArena Text: Spanish | Not measured | 1400 | Only Qwen3.5-Flash measured |
| LMArena Text: Russian | 1435 | 1380 | DeepSeek V3.1 Terminus |
| LMArena Text: Polish | 1427 | 1376 | DeepSeek V3.1 Terminus |
| LMArena Text: multi-turn | 1411 | 1393 | DeepSeek V3.1 Terminus |
| LMArena Text: longer queries | 1422 | 1392 | DeepSeek V3.1 Terminus |
| LMArena Text: writing and language | 1409 | 1369 | DeepSeek V3.1 Terminus |
| LMArena Text: entertainment and media | 1391 | 1347 | DeepSeek V3.1 Terminus |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Qwen3.5-Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.5-Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-Flash costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V3.1 Terminus | Qwen3.5-Flash |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | No public weights listed |
| Released | September 22, 2025 | February 25, 2026 |
| Context window | 164K | 1M |
| Input $/Mtok | $0.3063 | $0.065 |
| Output $/Mtok | $1.0142 | $0.26 |
| Coding index | Not benchmarked | 41.1 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 Terminus better than Qwen3.5-Flash for coding?
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; DeepSeek V3.1 Terminus has none, so the two cannot be ranked on coding. Pick DeepSeek V3.1 Terminus for work measured by ALE-Bench or LMArena Text: coding, where it leads.
Which is cheaper, DeepSeek V3.1 Terminus or Qwen3.5-Flash?
DeepSeek V3.1 Terminus costs $0.3063 per million input tokens and $1.0142 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Cost per solved task is known for Qwen3.5-Flash only.
Which has the bigger context window, DeepSeek V3.1 Terminus or Qwen3.5-Flash?
Qwen3.5-Flash, with 1M tokens against 164K.
How many benchmarks is the DeepSeek V3.1 Terminus vs Qwen3.5-Flash comparison based on?
23 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1 Terminus's coding index rests on 0 independent benchmarks and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.