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Model profile
Inception
New in 2026

Inception: Mercury 2

Inception: Mercury 2 is a budget-priced text-first model from Inception with heavy runtime profile, extended context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsHeavy latencyExtended contextBudget pricing
Agentic models
Intelligence
32.8

Benchmark blend

Coding
30.6

Dev workflow signal

Context
128K Tokens

Extended

Input Price
$0.25

Budget tier

Decision snapshot
51

Inception: Mercury 2 currently reads as a budget text-first option with extended context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Heavy
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis
Open compare hubSee 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
32.8
33

General reasoning and benchmark headroom.

Limited
Speed
981 tok/s
50

TTFT 4.57s

Situational
Context
128K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.25
86

$0.75 output / 1M

Efficient

Editorial Profile

Inception: Mercury 2 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 31Math score N/A

Mercury 2 is an extremely fast reasoning LLM, and the first reasoning diffusion LLM (dLLM). Instead of generating tokens sequentially, Mercury 2 produces and refines multiple tokens in parallel, achieving...

Identity

Inception text-first profile

Positioning

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

Cost posture

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

Strengths

  • Large context headroom supports repo-wide prompts and long research sessions.

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

  • Long-context summarization, repo analysis, and policy or document review.

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
32.8
GPQA
77.0%
HLE
15.5%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
30.6
SciCode
38.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
69.8%
TAU2
70.8%
TerminalBench Hard
26.5%
LCR
36.3%

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
128K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Other
Max Completion
50000
Moderation
No
Supported Parameters
include_reasoningmax_tokensreasoningresponse_formatstopstructured_outputstemperaturetool_choicetools
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.25
Output
per 1M output tokens
$0.75
Blended
AA 3:1 mix
$0.38

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

OR Cache Read
$0.00

Metadata

Raw source tables at the end

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