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Model profile
DeepSeek

DeepSeek V3.1 Terminus (Reasoning)

DeepSeek V3.1 Terminus (Reasoning) is a budget text-first model from DeepSeek with a heavy runtime profile, unknown context posture, and the clearest fit around agent workflows / long-context research.

Best for: Agent workflows / Long-context researchHeavy latencyUnknown contextBudget pricing
Intelligence
33.9

Benchmark blend

Coding
33.7

Dev workflow signal

Context
N/A

Unknown

Input Price
$0.40

Budget tier

Decision snapshot
44

DeepSeek V3.1 Terminus (Reasoning) currently reads as a budget text-first option with unknown context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Long-context research
Latency tier
Heavy
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis
Compare this modelSee leaderboard

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
33.9
34

General reasoning and benchmark headroom.

Limited
Speed
N/A
46

Latency data is partial.

Situational
Context
N/A
34

How much prompt and task state can stay in view.

Limited
Price
$0.40
86

$2.00 output / 1M

Efficient

Editorial Profile

DeepSeek V3.1 Terminus (Reasoning) in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 34Math score 90

The DeepSeek V3.1 Terminus (Reasoning) AI model by DeepSeek.

Identity

DeepSeek text-first profile

Positioning

Agent workflows / Long-context research with unknown context and heavy runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths

  • The available source data suggests a balanced profile rather than one dominant edge.

Tradeoffs

  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency profile is better for deliberate runs than rapid back-and-forth chat.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit

  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
33.9
MMLU Pro
85.1%
GPQA
79.2%
HLE
15.2%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
33.7
LiveCodeBench
0.798
SciCode
40.6%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
89.7
AIME 2025
89.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
57.0%
TAU2
37.1%
TerminalBench Hard
30.3%
LCR
65.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$0.40
Output
per 1M output tokens
$2.00
Blended
AA 3:1 mix
$0.80

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.