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DeepSeek V3.1 Terminus vs Qwen3.5-122B-A10B

Qwen3.5-122B-A10B leads DeepSeek V3.1 Terminus on the Artificial Analysis Intelligence Index, 17.7 to 14.8, and DeepSeek V3.1 Terminus costs 2.1x less per output token ($1.0142 vs $2.08 per million). Updated October 22, 2026; 31 benchmarks have a result for both.

Replace DeepSeek V3.1 Terminus Replace Qwen3.5-122B-A10B
DeepSeek V3.1 TerminusDeepSeek
Coding
No data
Overall
No data
Output $/M
$1.0142
18
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
13
vs boards won of 31 shared

DeepSeek V3.1 Terminus or Qwen3.5-122B-A10B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick DeepSeek V3.1 Terminus for output tokens 2.1x cheaper ($1.0142 vs $2.08 per million) and work measured by SciCode or LMArena Text: coding, where it leads.

Model ADeepSeek V3.1 TerminusDeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-122B-A10BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
31
DeepSeek V3.1 Terminus only
14
Qwen3.5-122B-A10B only
20
Like-for-like categories
7 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.

At a glance

Coding index
DeepSeek V3.1 Terminus not benchmarked, Qwen3.5-122B-A10B not benchmarked.
Widest category
Agentic work, 56.4 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.1 Terminus 37.1% against Qwen3.5-122B-A10B 93.6%, 56.4 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.1 Terminus $1.0142 against Qwen3.5-122B-A10B $2.08 per million output tokens, 2.1x apart.
Context window
DeepSeek V3.1 Terminus takes 164K tokens, Qwen3.5-122B-A10B 262K.

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.

Boards won, where both have a result
CategoryDeepSeek V3.1 TerminusBarsQwen3.5-122B-A10BTies
Coding23
Intelligence01
Agentic work01
Reasoning22
Math01
Knowledge40
General105
All shared boards1813
CodingLike-for-likeSix of these boards feed the Vibe Coding Index23
Coding benchmarks for DeepSeek V3.1 Terminus and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.1 TerminusQwen3.5-122B-A10BLeader
LMArena WebDevNot measured1361Only Qwen3.5-122B-A10B measured
AA Coding Index43.543.3DeepSeek V3.1 Terminus
LiveCodeBench (Artificial Analysis run)79.8%Not measuredOnly DeepSeek V3.1 Terminus measured
SciCode38.0%39.7%Qwen3.5-122B-A10B
Terminal-Bench Hard (Artificial Analysis run)31.8%31.1%DeepSeek V3.1 Terminus
ALE-Bench745.2Not measuredOnly DeepSeek V3.1 Terminus measured
LMArena Text: coding14251436Qwen3.5-122B-A10B
LMArena Text: software and IT14261436Qwen3.5-122B-A10B
LMArena WebDev: ReactNot measured1353Only Qwen3.5-122B-A10B measured
LMArena WebDev: HTMLNot measured1351Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 33 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for DeepSeek V3.1 Terminus and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.1 TerminusQwen3.5-122B-A10BLeader
AA Intelligence Index14.817.7Qwen3.5-122B-A10B
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.1 Terminus and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.1 TerminusQwen3.5-122B-A10BLeader
AA Agentic Index6.8Not measuredOnly DeepSeek V3.1 Terminus measured
τ²-Bench Telecom37.1%93.6%Qwen3.5-122B-A10B
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index22
Reasoning benchmarks for DeepSeek V3.1 Terminus and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.1 TerminusQwen3.5-122B-A10BLeader
GPQA Diamond (Artificial Analysis run)79.2%85.7%Qwen3.5-122B-A10B
Humanity's Last Exam (Artificial Analysis run)16.4%25.2%Qwen3.5-122B-A10B
Mystery game puzzlesNot measured17.0%Only Qwen3.5-122B-A10B measured
LMArena Text: hard prompts14261421DeepSeek V3.1 Terminus
LMArena Text: expertNot measured1431Only Qwen3.5-122B-A10B measured
LMArena Text: hard prompts in English14321431DeepSeek V3.1 Terminus
LMArena Vision: diagramsNot measured1249Only Qwen3.5-122B-A10B measured
LMArena Vision: homeworkNot measured1261Only Qwen3.5-122B-A10B measured
Kagi LLM Benchmark57.4%Not measuredOnly DeepSeek V3.1 Terminus measured
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index01
Math benchmarks for DeepSeek V3.1 Terminus and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.1 TerminusQwen3.5-122B-A10BLeader
AA Math Index89.7Not measuredOnly DeepSeek V3.1 Terminus measured
AIME 2025 (Artificial Analysis run)89.7%Not measuredOnly DeepSeek V3.1 Terminus measured
LMArena Text: math14021431Qwen3.5-122B-A10B
LMArena Text: mathematical workNot measured1431Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for DeepSeek V3.1 Terminus and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.1 TerminusQwen3.5-122B-A10BLeader
MMLU Pro (Artificial Analysis run)85.1%Not measuredOnly DeepSeek V3.1 Terminus measured
LMArena Text: science14511432DeepSeek V3.1 Terminus
LMArena Text: medicine and healthcare14681423DeepSeek V3.1 Terminus
LMArena Text: legal and government14571422DeepSeek V3.1 Terminus
LMArena Text: business and finance14141414DeepSeek V3.1 Terminus
Neither model has a result on the other 15 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for DeepSeek V3.1 Terminus and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.1 TerminusQwen3.5-122B-A10BLeader
Design Arena: websites1187Not measuredOnly DeepSeek V3.1 Terminus measured
Design Arena: UI components1180Not measuredOnly DeepSeek V3.1 Terminus measured
Design Arena: game dev1131Not measuredOnly DeepSeek V3.1 Terminus measured
Design Arena: data viz1168Not measuredOnly DeepSeek V3.1 Terminus measured
Design Arena: 3D1141Not measuredOnly DeepSeek V3.1 Terminus measured
Design Arena: all code categories1175Not measuredOnly DeepSeek V3.1 Terminus measured
Design Arena: SVG1065Not measuredOnly 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 index105
General benchmarks for DeepSeek V3.1 Terminus and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.1 TerminusQwen3.5-122B-A10BLeader
IFBench57.0%75.7%Qwen3.5-122B-A10B
Long context reasoning (AA-LCR)69.3%76.3%Qwen3.5-122B-A10B
DTBench81.3%84.3%Qwen3.5-122B-A10B
LMCA28.6%32.2%Qwen3.5-122B-A10B
LMArena Text14191417DeepSeek V3.1 Terminus
LMArena Text: instruction following14041399DeepSeek V3.1 Terminus
LMArena Text: creative writing14031368DeepSeek V3.1 Terminus
LMArena VisionNot measured1244Only Qwen3.5-122B-A10B measured
LMArena Text: English14291428DeepSeek V3.1 Terminus
LMArena Text: non-English14081401DeepSeek V3.1 Terminus
LMArena Text: ChineseNot measured1463Only Qwen3.5-122B-A10B measured
LMArena Text: GermanNot measured1425Only Qwen3.5-122B-A10B measured
LMArena Text: JapaneseNot measured1367Only Qwen3.5-122B-A10B measured
LMArena Text: KoreanNot measured1353Only Qwen3.5-122B-A10B measured
LMArena Text: FrenchNot measured1443Only Qwen3.5-122B-A10B measured
LMArena Text: SpanishNot measured1425Only Qwen3.5-122B-A10B measured
LMArena Text: Russian14351400DeepSeek V3.1 Terminus
LMArena Text: Polish14271398DeepSeek V3.1 Terminus
LMArena Text: multi-turn14111416Qwen3.5-122B-A10B
LMArena Text: longer queries14221410DeepSeek V3.1 Terminus
LMArena Text: writing and language14091387DeepSeek V3.1 Terminus
LMArena Text: entertainment and media13911366DeepSeek V3.1 Terminus
LMArena Vision: EnglishNot measured1249Only Qwen3.5-122B-A10B measured
LMArena Vision: ChineseNot measured1301Only Qwen3.5-122B-A10B measured
LMArena Vision: reading textNot measured1251Only Qwen3.5-122B-A10B measured
LMArena Vision: humorNot measured1243Only Qwen3.5-122B-A10B measured
LMArena Vision: creative writingNot measured1220Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.1 Terminus and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.1 TerminusQwen3.5-122B-A10BLeader
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

DeepSeek V3.1 Terminus$0.0008Fits in one request
Qwen3.5-122B-A10B$0.0013Fits in one request

DeepSeek V3.1 Terminus costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.1 Terminus$0.018Fits in one request
Qwen3.5-122B-A10B$0.019Fits in one request

DeepSeek V3.1 Terminus costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.1 Terminus$0.071Exceeds the context window
Qwen3.5-122B-A10B$0.073Fits in one request

DeepSeek V3.1 Terminus 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.

Specifications of DeepSeek V3.1 Terminus and Qwen3.5-122B-A10B
SpecificationDeepSeek V3.1 TerminusQwen3.5-122B-A10B
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedSeptember 22, 2025February 25, 2026
Context window164K262K
Input $/Mtok$0.3063$0.26
Output $/Mtok$1.0142$2.08
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.1 Terminus better than Qwen3.5-122B-A10B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.1 Terminus for output tokens 2.1x cheaper ($1.0142 vs $2.08 per million) and work measured by SciCode or LMArena Text: coding, where it leads.

Which is cheaper, DeepSeek V3.1 Terminus or Qwen3.5-122B-A10B?

DeepSeek V3.1 Terminus costs $0.3063 per million input tokens and $1.0142 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V3.1 Terminus or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B, with 262K tokens against 164K.

How many benchmarks is the DeepSeek V3.1 Terminus vs Qwen3.5-122B-A10B comparison based on?

31 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 Qwen3.5-122B-A10B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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