GPT-6.1 Terra vs Claude Opus 5.5: In-Depth Comparison

GPT-6.1 Terra vs Claude Opus 5.5: 1.05M Context vs Frontier Reasoning

GPT-6.1 Terra provides higher execution speed and significantly cheaper token rates for desktop computer use, while Claude Opus 5.5 delivers superior general intelligence, creative precision, and multi-step architectural reasoning; the right pick depends on whether your priority is raw inference budget or frontier task accuracy.

Source: @haider1

The arrival of GPT-6.1 Terra represents an architectural pivot by OpenAI. Rather than attempting to push absolute intelligence frontiers past Anthropic's flagship, OpenAI designed Terra as a high-throughput workhorse. It explicitly targets visual reasoning, UI manipulation, and automated evaluation at a fraction of standard API prices. Claude Opus 5.5 remains Anthropic's pinnacle reasoning system, holding top positions across composite intelligence benchmarks. Comparing these two models requires balancing output quality against running costs.

Key Takeaways

• Intelligence Hierarchy: Claude Opus 5.5 holds the #1 spot on the Artificial Analysis Intelligence Index v5 with a score of 58 (Max effort), whereas GPT-6.1 Terra peaks at 48 on v4.3.2 and 51 on v5.
• Economic Disparity: GPT-6.1 Terra costs $1.00 per million input tokens and $5.00 per million output tokens, operating roughly 4.5x to 9x cheaper per task than Claude Opus 5.5 on agentic benchmarks.
• Latency and Throughput: GPT-6.1 Terra processes computer-use tasks over 3x faster than traditional flagship models, clocking output speeds around 91 tokens per second on standard API tracks.
• Context Capacity: GPT-6.1 Terra supports a 1,050,000-token context window with a 128,000-token output limit, with 2x input pricing kicking in past 272k tokens.
• Workload Alignment: Claude Opus 5.5 remains mandatory for complex code generation, full repository maintenance, and creative frontend design; GPT-6.1 Terra excels as an operational subagent, automated judge, and screen automation driver.

GPT-6.1 Terra vs Claude Opus 5.5 at a Glance

The following table summarizes the verified technical specifications, pricing structures, and benchmark metrics for both models based on public vendor documentation and Artificial Analysis leaderboard data.

Metric / Dimension	GPT-6.1 Terra	Claude Opus 5.5	Source / Basis
Developer	OpenAI	Anthropic	Official vendor release
Model Category	Balanced Mid-Tier / Computer Use	Frontier Flagship Reasoning	Architectural placement
Context Window	1,050,000 tokens (1.05M)	unverified — no public number yet	Official specs (Terra) / Vendor pending (Opus)
Max Output Tokens	128,000 tokens (128K)	unverified — no public number yet	OpenAI API documentation
Standard Input Price	$1.00 / 1M tokens	unverified — no public number yet	OpenAI standard API pricing
Standard Output Price	$5.00 / 1M tokens	unverified — no public number yet	OpenAI standard API pricing
Cached Input Price	$0.05 / 1M tokens	unverified — no public number yet	Official developer specs
AA Intelligence Index v5	51 (Max effort)	58 (Max effort)	Artificial Analysis Index v5
AA Intelligence Index v4.3.2	48 (Max effort)	58 (Max effort)	Artificial Analysis Release Card
Coding Agent Index v2.0	48 (Max effort)	unverified — no public number yet	Artificial Analysis Index
Terminal-Bench 4.0	50.3%	unverified — no public number yet	Vendor launch benchmarks
Agents' Last Exam Cost	~$0.40 – $1.00 per task	~$10.04 per task (Max effort)	Artificial Analysis / Community evals
Inference Output Speed	~91 tokens/sec (standard)	~98 tokens/sec (reported by AA)	Artificial Analysis runtime telemetry
Primary Sweet Spot	UI Actions, Subagents, Form Entry	Deep Reasoning, Architecture, Writing	Observed performance profiles

Intelligence and Benchmark Performance

On composite intelligence benchmarks, Claude Opus 5.5 demonstrates clear superiority over GPT-6.1 Terra. In the Artificial Analysis Intelligence Index v5, Claude Opus 5.5 retains first place globally with a score of 58 at Max reasoning effort. By comparison, GPT-6.1 Terra reaches a score of 51 at Max effort in the v5 update, trailing both Claude Opus 5.5 and OpenAI's higher-tier models such as GPT-6 Astra (57) and GPT-6.1 Sol (55). In earlier v4.3.2 evaluations, GPT-6.1 Terra registered a score of 48 at Max effort.

The performance profile of GPT-6.1 Terra depends heavily on user-configured reasoning effort settings. On the Artificial Analysis scale, Terra records the following tiered outputs:

• Max Effort: 48 points ($0.40 average cost per task)
• XHigh Effort: 45 points ($0.19 average cost per task)
• High Effort: 44 points ($0.13 average cost per task)
• Medium Effort: 41 points ($0.10 average cost per task)
• Low Effort: 36 points ($0.05 average cost per task)
• Non-Reasoning: 33 points ($0.11 average cost per task)

Artificial Analysis notes that the Low and Medium effort tiers of GPT-6.1 Terra are Pareto-optimal for cost efficiency against the broader market. However, pushed to Max effort, the model hits diminishing returns where higher-tier models like GPT-6.1 Sol provide cleaner execution per dollar.

Anthropic's Claude Opus 5.5 shows wider dynamic range across its reasoning effort spectrum. According to leaderboard data, Opus 5.5 spans from 35 points at low effort ($0.98 per task) up to 58 points at Max effort ($10.04 per task). When deep multi-branch planning is required, Claude Opus 5.5 operates in a performance tier that GPT-6.1 Terra cannot match.

In specialized terminal environments, the gap narrows depending on domain. On Terminal-Bench 4.0, GPT-6.1 Terra achieves 50.3%, doubling the 21.5% score of its predecessor GPT-5.6 Terra. Community reporting highlights that on Terminal-Bench-Science tasks, Terra comes within 5 points of Claude Opus 5.5. Factual accuracy benchmarks also indicate that GPT-6.1 Terra reduced its hallucination error rate in AA-Omniscience tests to 38% (down from 88% in GPT-5.6 Terra), with vendor tests showing a factual error drop to 4.3%.

Source: @HarshithLucky3

Even with these gains, evaluations measuring open-ended software design tell a different story. In independent community testing involving 100 self-contained HTML experiments, GPT-6.1 Terra exhibited repetitive structures and instruction-following failures, lagging behind Claude models.

Computer Use, Agent Automation, and Subagent Economics

Where GPT-6.1 Terra establishes clear differentiation against Claude Opus 5.5 is in agentic desktop operations and computer-use automation. OpenAI specifically trained GPT-6.1 Terra for visual reasoning across graphical interfaces, software interaction, and rapid tool calling via the Responses API.

Engineering teams at Microsoft integrated gpt-6.1-terra as an automated evaluation judge across 18 testing configurations, replacing GPT-5.6 Terra to review outputs generated by GPT-6.1 Sol. GitHub Copilot engineers similarly conducted production tests using Terra for automated pull request checks, code reviews, and CI integration at high reasoning effort.

Real-world execution tests illustrate the operational trade-offs between Terra's speed and Opus's precision:

• 3D Modeling and Scene Synthesis: In a comparative generation test constructing a 3D restaurant scene, GPT-6.1 Terra completed the assignment in 40 minutes at an estimated cost of $0.73. Claude Opus 5.5 took 83 minutes and cost $3.35, though community observers credited Opus with superior lighting and reflection rendering.
• Interactive Mechanical Demos: In an engine-disassembly instructional build across 4-stroke cycle phases, GPT-6.1 Terra completed the pipeline in 43 minutes for $4.90. Claude Opus 5.5 took 2 hours and 45 minutes for $31.85, representing a 6.5x cost difference and roughly 4x speed improvement for Terra.
• Browser and Desktop Navigation: OpenAI internal benchmarks on Agents' Last Exam indicate that GPT-6.1 Terra finishes computer-use tasks over 3x faster than GPT-6 Astra, operating at approximately one-ninth the per-task cost of Claude Opus 5.5 at equivalent settings.

As analyzed in our overview of what GPT-6.1 Terra represents in enterprise evaluation configs, Terra is rarely deployed to invent complex systems from scratch. Instead, it functions as an affordable UI actuator, handling form fills, invoice extraction, and programmatic browser driving where deploying Claude Opus 5.5 would be economically prohibitive.

Pricing Structures, Token Rates, and Long-Context Thresholds

The economic architectures of these models serve entirely different operational budgets.

OpenAI publishes fixed standard token pricing for GPT-6.1 Terra:

• Standard Input: $1.00 per million tokens
• Standard Output: $5.00 per million tokens
• Cached Input: $0.05 per million tokens (95% discount)
• Cache Writes (30-min retention): $1.25 per million tokens
• Cache Writes (12-hour retention): $2.50 per million tokens

Terra enforces a specific tiered surcharge for long prompts: requests exceeding 272,000 input tokens bill the entire execution at twice the input rate ($2.00/1M) and 1.5 times the output rate ($7.50/1M). Discount modes include Batch and Flex processing, which reduce base rates by 50%, while Fast mode doubles baseline pricing.

Public API per-token rates for Claude Opus 5.5 remain unverified in vendor sheets, but aggregate task costs published by Artificial Analysis place Claude Opus 5.5 Max runs at an average of $10.04 per task. Across comparable complex scenarios, Claude Opus 5.5 consumes 8x to 25x more budget per run than Terra's $0.40 to $1.27 range.

For development teams evaluating the broader frontier ecosystem, our previous breakdown comparing DeepSeek V4.1 Flash and Claude Opus 5.5 highlighted how massive pricing deltas alter architecture choices. The exact same dynamic applies to GPT-6.1 Terra: running multi-agent orchestrations with Opus 5.5 at every tier drains budgets within days, making a high-speed model like Terra valuable as a frontline worker.

Execution Latency and Throughput

System throughput diverges sharply between the two architectures. In Artificial Analysis standardized measurements, GPT-6.1 Terra outputs 90.9 to 91 tokens per second across its reasoning tiers, with non-reasoning variants exhibiting an initial Time to First Token (TTFT) of approximately 1.12 seconds.

Individual community measurements report localized generation bursts:

• Standard generation loops observe steady throughput between 22 and 23 tokens per second in high-load chat interfaces, with clean API endpoints hitting 55 to 120 tokens per second depending on concurrency.
• In desktop application control and CAD drafting tests, developers noted that Terra operates faster unassisted than human manual navigation across multi-window tasks.
• In subscription interfaces (ChatGPT Work and Codex), user quotas on Terra drain slowly compared to frontier flagships, with 30 minutes of continuous generation consuming as little as 2% of standard rolling allowances.

Claude Opus 5.5 exhibits strong token generation throughput (measured at ~98 tokens per second on Artificial Analysis), but its deep chain-of-thought exploration leads to significantly longer wall-clock completion times on agent tasks. When an agent must verify intermediate UI states across 50 consecutive browser steps, Opus's longer deliberation cycles cause multi-hour runtimes, whereas Terra completes equivalent sequences in under 45 minutes.

Which One Should You Use?

Choosing between GPT-6.1 Terra and Claude Opus 5.5 requires matching your application requirements to either execution economy or reasoning depth.

Choose GPT-6.1 Terra if:

• You are building automated desktop or browser agents: Your system needs to read UI screenshots, interact with software buttons, process tabular invoices, or parse CAD drawings with low latency.
• You need high-volume automated evaluation: You are implementing CI/CD code judges, grading pipelines, or subagent filtering where per-call costs must remain near $1.00 per million input tokens.
• Your budget demands maximum token mileage: You want a model supported across paid ChatGPT tiers, Codex, and enterprise endpoints that will not rapidly deplete operational allowances.
• Task completion speed is critical: Your agentic loops require sub-minute response cycles rather than deep, multi-hour reflective deliberation.

Choose Claude Opus 5.5 if:

• You require state-of-the-art coding and architecture: You are maintaining multi-file codebases, building complex full-stack web applications, or designing core algorithmic structures where lower-tier models hit retry limits.
• Benchmark precision is non-negotiable: Your workload targets the top of the Artificial Analysis Intelligence Index (58 points) for mission-critical analytical rigor.
• You are producing nuanced creative or technical writing: You need sophisticated tone moderation, varied frontend layouts, and deep instruction adherence without repetitive visual patterns.
• You are conducting heavy mathematical or scientific research: You need frontier-level synthetic deduction where cost per task is secondary to answer validity.

Frequently Asked Questions

Is GPT-6.1 Terra better than Claude Opus 5.5?

No, Claude Opus 5.5 remains superior in overall frontier intelligence, leading the Artificial Analysis Intelligence Index v5 with a score of 58 compared to 48 or 51 for GPT-6.1 Terra Max. GPT-6.1 Terra only holds advantages in task execution speed, prompt processing latency, and execution cost.

Is GPT-6.1 Terra cheaper than Claude Opus 5.5?

Yes, GPT-6.1 Terra is significantly cheaper. Its standard API rate is $1.00 per million input tokens and $5.00 per million output tokens, which results in task costs roughly 4.5x to 9x lower than Claude Opus 5.5 across complex agentic evaluations.

How does GPT-6.1 Terra compare to Claude Opus 5.5 in coding?

Claude Opus 5.5 outperforms GPT-6.1 Terra on complex software engineering, repository-level debugging, and sophisticated web generation. OpenAI positions GPT-6.1 Sol and GPT-6 Astra for difficult coding, while GPT-6.1 Terra focuses primarily on shell execution, CI review, and routine desktop workflows.

What are the context window limits for both models?

GPT-6.1 Terra features a context window of 1,050,000 tokens with up to 128,000 max output tokens. Public context limits for Claude Opus 5.5 vary by platform tier, while its exact long-prompt scaling multipliers remain unverified in this test suite.

Can GPT-6.1 Terra replace Claude Opus 5.5 in agentic workflows?

GPT-6.1 Terra can replace Claude Opus 5.5 for high-frequency browser navigation, form entry, desktop UI manipulation, and subagent evaluation pipelines. It is not recommended as a replacement for high-stakes autonomous reasoning, difficult mathematical proofs, or multi-file architecture planning.

Where is GPT-6.1 Terra available to developers?

GPT-6.1 Terra is accessible through the OpenAI API, ChatGPT Work, and Codex for paid enterprise and personal tiers, as well as via API providers including the kie.ai GPT 6.1 Terra API.

What to Watch Next

As adoption patterns settle, monitor whether OpenAI deploys a corresponding Astra-tier update to challenge Opus 5.5 directly at the top of the Intelligence Index. Keep track of independent coding leaderboards such as SWE-bench Verified and Code Arena to see if Terra receives dedicated agentic scores, and observe whether Anthropic updates its pricing tiers for long-context desktop automation.

Try related models on Kie.ai: GPT 6.1 Terra, Claude Opus 5.5 and GPT 6.1 Sol.