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DeepSeek V3.1 Terminus vs MiniMax M2.7

MiniMax M2.7 leads DeepSeek V3.1 Terminus on the Artificial Analysis Intelligence Index, 22.8 to 14.8, at about the same output price ($1.20 vs $1.0142 per million). Updated October 22, 2026; 38 benchmarks have a result for both.

Replace DeepSeek V3.1 Terminus Replace MiniMax M2.7
DeepSeek V3.1 TerminusDeepSeek
Coding
No data
Overall
No data
Output $/M
$1.0142
15
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
23
vs boards won of 38 shared

DeepSeek V3.1 Terminus or MiniMax M2.7: which to pick

Only MiniMax M2.7 has a coding score, 36.2 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 output tokens 1.2x cheaper ($1.0142 vs $1.20 per million) and work measured by ALE-Bench or LMArena Text: hard prompts, where it leads.

Model ADeepSeek V3.1 TerminusDeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
38
DeepSeek V3.1 Terminus only
7
MiniMax M2.7 only
36
Like-for-like categories
8 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, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Agentic work, 28.1 points apart on average over 2 shared benchmarks.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.1 Terminus 37.1% against MiniMax M2.7 84.8%, 47.7 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.1 Terminus $1.0142 against MiniMax M2.7 $1.20 per million output tokens, 1.2x apart.
Context window
DeepSeek V3.1 Terminus takes 164K tokens, MiniMax M2.7 205K.

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 TerminusBarsMiniMax M2.7Ties
Coding15
Intelligence01
Agentic work02
Reasoning13
Math01
Knowledge40
Web design07
General94
All shared boards1523
CodingLike-for-likeSix of these boards feed the Vibe Coding Index15
Coding benchmarks for DeepSeek V3.1 Terminus and MiniMax M2.7
BenchmarkDeepSeek V3.1 TerminusMiniMax M2.7Leader
LMArena WebDevNot measured1399Only MiniMax M2.7 measured
AA Coding Index43.552.6MiniMax M2.7
LiveCodeBench (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
SWE-bench Verified (Vals AI)Not measured73.8%Only MiniMax M2.7 measured
Vibe Code Bench (Vals AI)Not measured11.9%Only MiniMax M2.7 measured
Code migration (Vals AI)Not measured8.7%Only MiniMax M2.7 measured
ProgramBench (Vals AI)Not measured0.0%Only MiniMax M2.7 measured
Terminal-Bench 2.1 (Vals AI)Not measured48.7%Only MiniMax M2.7 measured
LiveCodeBench (Artificial Analysis run)79.8%Not measuredOnly DeepSeek V3.1 Terminus measured
SciCode38.0%50.1%MiniMax M2.7
Terminal-Bench Hard (Artificial Analysis run)31.8%39.4%MiniMax M2.7
Terminal-BenchNot measured45.1%Only MiniMax M2.7 measured
WeirdMLNot measured37.0%Only MiniMax M2.7 measured
ALE-Bench745.2599.3DeepSeek V3.1 Terminus
LMArena Text: coding14251454MiniMax M2.7
LMArena Text: software and IT14261446MiniMax M2.7
LMArena WebDev: ReactNot measured1388Only MiniMax M2.7 measured
LMArena WebDev: HTMLNot measured1401Only MiniMax M2.7 measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for DeepSeek V3.1 Terminus and MiniMax M2.7
BenchmarkDeepSeek V3.1 TerminusMiniMax M2.7Leader
AA Intelligence Index14.822.8MiniMax M2.7
Epoch Capabilities IndexNot measured145.8Only MiniMax M2.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for DeepSeek V3.1 Terminus and MiniMax M2.7
BenchmarkDeepSeek V3.1 TerminusMiniMax M2.7Leader
AA Agentic Index6.815.3MiniMax M2.7
Finance Agent (Vals AI)Not measured27.9%Only MiniMax M2.7 measured
Tax Agent Bench (Vals AI)Not measured6.9%Only MiniMax M2.7 measured
Legal Research Bench (Vals AI)Not measured10.6%Only MiniMax M2.7 measured
Excel Modeling Benchmark (Vals AI)Not measured28.4%Only MiniMax M2.7 measured
τ²-Bench Telecom37.1%84.8%MiniMax M2.7
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index13
Reasoning benchmarks for DeepSeek V3.1 Terminus and MiniMax M2.7
BenchmarkDeepSeek V3.1 TerminusMiniMax M2.7Leader
GPQA DiamondNot measured86.6%Only MiniMax M2.7 measured
GPQA Diamond (Artificial Analysis run)79.2%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)16.4%29.6%MiniMax M2.7
LMArena Text: hard prompts14261422DeepSeek V3.1 Terminus
LMArena Text: expertNot measured1443Only MiniMax M2.7 measured
LMArena Text: hard prompts in English14321441MiniMax M2.7
Kagi LLM Benchmark57.4%Not measuredOnly DeepSeek V3.1 Terminus measured
Neither model has a result on the other 16 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index01
Math benchmarks for DeepSeek V3.1 Terminus and MiniMax M2.7
BenchmarkDeepSeek V3.1 TerminusMiniMax M2.7Leader
AIMENot measured91.0%Only MiniMax M2.7 measured
AA Math Index89.7Not measuredOnly DeepSeek V3.1 Terminus measured
AIME 2025 (Artificial Analysis run)89.7%Not measuredOnly DeepSeek V3.1 Terminus measured
ProofBenchNot measured3.0%Only MiniMax M2.7 measured
LMArena Text: math14021421MiniMax M2.7
LMArena Text: mathematical workNot measured1437Only MiniMax M2.7 measured
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for DeepSeek V3.1 Terminus and MiniMax M2.7
BenchmarkDeepSeek V3.1 TerminusMiniMax M2.7Leader
MMLU ProNot measured80.4%Only MiniMax M2.7 measured
LegalBench (Vals AI)Not measured84.0%Only MiniMax M2.7 measured
CaseLaw (Vals AI)Not measured60.9%Only MiniMax M2.7 measured
CorpFin (Vals AI)Not measured61.2%Only MiniMax M2.7 measured
TaxEval (Vals AI)Not measured66.6%Only MiniMax M2.7 measured
MedCode (Vals AI)Not measured34.4%Only MiniMax M2.7 measured
MMLU Pro (Artificial Analysis run)85.1%Not measuredOnly DeepSeek V3.1 Terminus measured
LMArena Text: science14511416DeepSeek V3.1 Terminus
LMArena Text: medicine and healthcare14681409DeepSeek V3.1 Terminus
LMArena Text: legal and government14571403DeepSeek V3.1 Terminus
LMArena Text: business and finance14141407DeepSeek V3.1 Terminus
Neither model has a result on the other 9 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for DeepSeek V3.1 Terminus and MiniMax M2.7
BenchmarkDeepSeek V3.1 TerminusMiniMax M2.7Leader
Design Arena: websites11871242MiniMax M2.7
Design Arena: UI components11801207MiniMax M2.7
Design Arena: game dev11311209MiniMax M2.7
Design Arena: data viz11681233MiniMax M2.7
Design Arena: 3D11411194MiniMax M2.7
Design Arena: all code categories11751233MiniMax M2.7
Design Arena: SVG10651137MiniMax M2.7
Design Arena: ASCII artNot measured1136Only MiniMax M2.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index94
General benchmarks for DeepSeek V3.1 Terminus and MiniMax M2.7
BenchmarkDeepSeek V3.1 TerminusMiniMax M2.7Leader
MedScribe (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
IFBench57.0%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)69.3%78.3%MiniMax M2.7
DTBench81.3%Not measuredOnly DeepSeek V3.1 Terminus measured
LMCA28.6%Not measuredOnly DeepSeek V3.1 Terminus measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text14191405DeepSeek V3.1 Terminus
LMArena Text: instruction following14041405MiniMax M2.7
LMArena Text: creative writing14031354DeepSeek V3.1 Terminus
LMArena Text: English14291426DeepSeek V3.1 Terminus
LMArena Text: non-English14081382DeepSeek V3.1 Terminus
LMArena Text: ChineseNot measured1442Only MiniMax M2.7 measured
LMArena Text: GermanNot measured1398Only MiniMax M2.7 measured
LMArena Text: JapaneseNot measured1260Only MiniMax M2.7 measured
LMArena Text: KoreanNot measured1314Only MiniMax M2.7 measured
LMArena Text: FrenchNot measured1424Only MiniMax M2.7 measured
LMArena Text: SpanishNot measured1402Only MiniMax M2.7 measured
LMArena Text: Russian14351383DeepSeek V3.1 Terminus
LMArena Text: Polish14271387DeepSeek V3.1 Terminus
LMArena Text: multi-turn14111412MiniMax M2.7
LMArena Text: longer queries14221418DeepSeek V3.1 Terminus
LMArena Text: writing and language14091376DeepSeek V3.1 Terminus
LMArena Text: entertainment and media13911360DeepSeek 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
Image generation benchmarks for DeepSeek V3.1 Terminus and MiniMax M2.7
BenchmarkDeepSeek V3.1 TerminusMiniMax M2.7Leader
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
MiniMax M2.7$0.0009Fits 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
MiniMax M2.7$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
MiniMax M2.7$0.072Exceeds the context window

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 MiniMax M2.7
SpecificationDeepSeek V3.1 TerminusMiniMax M2.7
MakerDeepSeekMiniMax
WeightsOpen weightsOpen weights
ReleasedSeptember 22, 2025March 18, 2026
Context window164K205K
Input $/Mtok$0.3063$0.30
Output $/Mtok$1.0142$1.20
Coding indexNot benchmarked36.2
Independent measurements behind it03
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.1 Terminus better than MiniMax M2.7 for coding?

Only MiniMax M2.7 has a coding score, 36.2 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 output tokens 1.2x cheaper ($1.0142 vs $1.20 per million) and work measured by ALE-Bench or LMArena Text: hard prompts, where it leads.

Which is cheaper, DeepSeek V3.1 Terminus or MiniMax M2.7?

DeepSeek V3.1 Terminus costs $0.3063 per million input tokens and $1.0142 per million output tokens; MiniMax M2.7 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.7 only.

Which has the bigger context window, DeepSeek V3.1 Terminus or MiniMax M2.7?

MiniMax M2.7, with 205K tokens against 164K.

How many benchmarks is the DeepSeek V3.1 Terminus vs MiniMax M2.7 comparison based on?

38 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 MiniMax M2.7's on 3. Where only one model has a result, the page says so instead of naming a winner.

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