Model profile
OpenAI
New in 2026

GPT-5.6 Terra (xhigh)

GPT-5.6 Terra (xhigh) is a mid-range-priced text-first model from OpenAI with heavy runtime profile, partial context coverage, and the clearest fit around agent workflows / coding.

Best for: Agent workflows / CodingHeavy latencyN/A contextMid-range pricing
Intelligence
52.8

Benchmark blend

Coding
70.6

Dev workflow signal

Context
N/A

N/A

Input Price
$2.00

Mid-range tier

Decision snapshot
55

GPT-5.6 Terra (xhigh) currently reads as a mid-range text-first option with partially published context and a heavy runtime profile.

Overall profile
Selective fit
Best for
Agent workflows / Coding
Latency tier
Heavy
Price tier
Mid-range
Source coverage
OpenRouterArtificial Analysis

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
52.8
53

General reasoning and benchmark headroom.

Situational
Speed
115 tok/s
41

TTFT 8.78s

Limited
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$2.00
62

$12.00 output / 1M

Competitive

Editorial Profile

GPT-5.6 Terra (xhigh) in one narrative

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

Selective fitCoding score 71Math score N/A

The GPT-5.6 Terra (xhigh) AI model by OpenAI.

Identity

OpenAI text-first profile

Positioning

Agent workflows / Coding with partially published context and heavy runtime.

Cost posture

Balanced spend profile. Easier to justify in mixed production and exploration workloads.

Strengths
  • Coding indicators point to a strong developer workflow fit.

Tradeoffs
  • Costs look manageable, but still deserve attention in always-on agents or batch jobs.

  • 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.

  • Context limits are only partially published, so long-session planning needs extra validation.

Best fit
  • Code generation, refactors, test writing, and tool-assisted debugging.

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Context
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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
52.8
GPQA
90.8%
HLE
41.9%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
70.6
SciCode
51.6%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
66.3%
TAU2
80.4%
TerminalBench Hard
62.9%
LCR
75.0%

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
$2.00
Output
per 1M output tokens
$12.00
Blended
AA 3:1 mix
$4.50

This model sits in a balanced spend range. It is easier to justify across both production and exploratory workflows.

Metadata

Raw source tables at the end

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