GPT-6.1 Terra and Claude Haiku 5.5 offer different ways to reduce the cost of useful AI work. Terra is the stronger starting point for demanding automation and terminal coding in the independent tests discussed below. Haiku offers substantially lower token prices and competitive professional knowledge-work performance.
For prompts up to 100K tokens, official standard API prices are $1 input / $5 output per million tokens for Terra and $0.10 / $0.50 for Haiku. Those rates make Haiku 90% cheaper per token, but reasoning, retries, caching, and long-context tiers determine what a completed task actually costs. Choose against a representative workload, rather than a single headline score.
Key Takeaways
GPT-6.1 Terra: Leads the cited independent max-effort comparison on automation and terminal coding; these results make it a useful candidate for complex agents.
Combines lower standard token prices with a lead on AA-Briefcase v1.1 in the same comparison.
Reviewed for clarity, source attribution and current API terminology.
Claude Haiku 5.5:
Long context: Terra supports 1.05M tokens and Haiku supports 1M; their higher pricing tiers begin above 272K and 100K input tokens, respectively.
Speed: Haiku has higher output throughput in the cited sample, while Terra has shorter first-token latency. Neither metric alone predicts a complete workflow.
Deployment: Compare successful-task cost, quality, latency, and retry rates at the reasoning settings you intend to use.
GPT-6.1 Terra vs Claude Haiku 5.5: At Glance
Specifications describe available capabilities, not a ranking of task quality. Both models accept images, but that does not establish that either wins every visual task. The context limit also does not guarantee reliable use of every detail in a long prompt.
GPT-6.1 Terra: Visual Work and Software Actions
According to OpenAI’s official announcement, the model combines stronger visual understanding, computer interaction, and natural writing with lower operating costs than its predecessor.
Rather than optimizing exclusively for conversational intelligence, Terra is designed to complete practical work involving documents, screens, and software applications.
Its main capabilities include:
Visual understanding: Analyzing screenshots, diagrams, product images, and technical drawings.
Computer use: Interacting with application interfaces and completing multi-step digital tasks.
Coding and debugging: Supporting command-line development, program generation, and automated software maintenance.
Professional writing: Producing reports, proposals, structured summaries, and business communications.
Configurable reasoning: Allowing developers to adjust reasoning depth for different quality and latency requirements.
In its internal factuality evaluation, OpenAI reports a 16.7% error rate at low effort and 4.3% at maximum effort. These are vendor-reported results on error-inducing prompts, not a measured error rate for every application.
The model is particularly interesting for developers who need more than fast text generation. Its value comes from connecting visual interpretation, tool use, and decision-making into a coherent workflow.
Claude Haiku 5.5: High-Volume, Focused Tasks
Released on October 7, 2026, Haiku 5.5 is designed to make capable reasoning models practical for applications that process large numbers of requests.
In its official introduction, Anthropic emphasizes classification, summarization, database queries, customer support, browser interactions, and subagent workflows.
Haiku 5.5 also introduces adjustable effort to the Haiku family. Developers can choose between lower-cost responses and deeper reasoning without changing models.
Its strengths include:
High-throughput processing: Efficiently handling large volumes of short and repetitive requests.
Adaptive thinking: Adjusting reasoning effort to the complexity of a task.
Knowledge-work performance: Strong results in professional document and business-task evaluations.
Subagent orchestration: Performing focused tasks within workflows coordinated by larger models.
Long-context processing: Supporting documents and conversation histories up to one million tokens.
Making classification, extraction, routing, and similar workloads commercially practical.
Anthropic reports 72.4% on the OSWorld 2.1 offline subset. This is a vendor evaluation on a specific subset, so it should not be treated as a controlled head-to-head desktop score against Terra.
This matters because Haiku 5.5 is no longer limited to answering simple questions. It can participate in more sophisticated agent workflows while retaining the pricing advantages of a compact model.
Haiku 5.5 uses a newer tokenizer than Haiku 4.5. Anthropic estimates that identical text can use approximately 30% more tokens than with its predecessor. Measure billable usage on your own prompts when estimating migration costs.
GPT-6.1 Terra vs Claude Haiku 5.5: Benchmark
Independent Benchmark Results
The independent results below use the Max variants displayed by Artificial Analysis, checked on October 30, 2026. Vendor launch scores appear separately because their test setups differ.
Automation and multi-step execution. Terra leads AutomationBench-AA by 25 percentage points. This suggests a stronger ability to complete workflows that require executing multiple actions, following instructions, and recovering from intermediate difficulties. An automation system is only useful when it completes the entire workflow.
Terminal-based coding. Terra achieves 56% on Terminal-Bench 4.0, compared with Haiku’s 33%. This is a meaningful advantage for tasks involving command-line tools, debugging, and autonomous software development. Nevertheless, smaller, narrowly scoped requests may favor Haiku’s lower cost and faster token generation.
Professional knowledge work. Haiku scores 1,576 on AA-Briefcase v1.1 versus Terra’s 1,402, making it a strong candidate for document and office workflows.
General factual reasoning. AA-Omniscience v1.1 scores Terra at 30 and Haiku at 12.
Official Launch Benchmarks
The vendors also published useful results for their respective models.
GPT-6.1 Terra vs Claude Haiku 5.5: Actual Task Performance
Coding and Software Engineering
For terminal agents, Terra has the clearer performance case: it scores 56% on Terminal-Bench 4.0 versus Haiku’s 33%, a 23-percentage-point gap. Start with Terra for debugging unfamiliar repositories, resolving dependencies, and running multi-step command-line workflows.
Haiku is a practical option for focused code explanations, test drafts, and isolated edits. At standard short-prompt rates, its output costs $0.50 per million tokens versus Terra’s $5; use accepted patches and passing tests to decide whether that saving survives retries.
Visual Understanding and Computer Use
Terra is designed for interpreting technical drawings, matching product images, and acting on application screens. Those capabilities suit workflows that move from visual evidence to software actions.
For high-volume support classification, extraction, and reply drafting, Haiku’s lower token prices provide a useful starting point. With 2,000 input and 300 output tokens per request, 10,000 requests cost $3.50 at Haiku’s official standard rates versus $35 for Terra, before caching, batch discounts, reasoning overhead, or tool fees.
For workflows that must execute actions across business systems, Terra’s 60% AutomationBench-AA score versus Haiku’s 35% supports testing Terra first. Invoice checks, reconciliation, and coordinated application updates are examples where a higher completion rate may justify a higher token price.
Long-Context Reasoning and Factual Reliability
Terra supports 1.05M context tokens and Haiku 1M. At 150,000 input and 2,000 output tokens, the official standard-rate examples below cost $0.160 and $0.080 respectively. Choose on evidence retrieval and accepted answers as well as request cost; the FAQ describes a long-context evaluation approach.
Is Claude Haiku 5.5 Faster Than GPT-6.1 Terra?
Haiku leads on output throughput in the cited Max comparison, while Terra starts responding sooner. The table below separates streaming speed, response latency, and the estimated time used by evaluation tasks.
Three separate metrics matter:
Time to first token: How long users wait before output begins.
Output throughput: How quickly tokens are generated after output starts.
End-to-end task completion: How long the model takes to finish the entire job, including reasoning and tool execution.
The current Max-versus-Max snapshot is from Artificial Analysis, checked on October 30, 2026.
Claude Haiku 5.5 delivers approximately 2.57 times higher output throughput, making it faster at generating tokens once decoding begins. However, GPT-6.1 Terra has a 93-second advantage in first-token latency and completes the 500-token response approximately 90 seconds sooner.
The difference becomes more interesting when considering reasoning efficiency. Haiku consumes approximately 4.4 times as many reasoning tokens per Intelligence Index task, despite its faster generation speed. Its weighted task decoding time is slightly shorter, but Terra's lower initial latency translates into faster responses in the 500-token test.
Comparison result: Haiku wins on raw generation speed, while Terra leads in first-token latency, fixed-length response time, and reasoning-token efficiency. Terra is the stronger choice for latency-sensitive, reasoning-intensive interactions, whereas Haiku offers higher token throughput.
End-to-End Task Completion
Haiku uses about 173K reasoning tokens per task versus Terra’s 39K. Its estimated task time is still lower in this snapshot: 598.04 versus 654.90 seconds. Throughput alone therefore does not describe the complete latency profile.
Real production latency depends on providers, server load, prompt size, tool execution, and reasoning configuration. Measure p50 and p95 on the same real requests before deciding.
The task-time figure estimates weighted decode time from token usage and generation speed; it excludes first-token latency and other overhead. Response-time measurements and evaluation-task estimates cover different workloads.
GPT-6.1 Terra vs Claude Haiku 5.5: Cost
Pricing is where the two models differ most dramatically.
Standard API Pricing
USD per million tokens, checked October 28, 2026. Haiku’s base rates apply to prompts up to 100K tokens. The listed CometAPI base input and output rates are 20% below the corresponding official rates for both models. Terra’s CometAPI catalog does not separately itemize a long-context tier or cache-read rate here; the standard discount should not be assumed to apply to an unpublished tier.
Official and CometAPI prices describe separate provider routes. For a budget, use the selected route’s billed input, output, cache, and tool categories. The worked examples below retain official standard pricing so their assumptions remain consistent.
Terra’s official long-prompt rates apply above 272K input tokens, and Haiku’s above 100K prompt tokens. Once a threshold is exceeded, use the applicable higher tier for the request. The worked examples below use official standard API rates.
For prompts of up to 100K tokens, Haiku’s official standard input and output token prices are 90% lower than Terra’s ($0.10 / $0.50 versus $1 / $5 per million tokens), excluding caching, batch discounts, and other charges. The saving changes for longer prompts because the pricing thresholds differ.
Example: High-Volume Classification
Consider an application that processes 10,000 customer messages daily. Assume each request uses 2,000 input tokens and 300 output tokens, with no prompt cache.
GPT-6.1 Terra: Input costs $0.0020 and output costs $0.0015 per request. The total is $0.0035 per request, or $35.00 for 10,000 requests.
Claude Haiku 5.5: Input costs $0.0002 and output costs $0.00015 per request. The total is $0.00035 per request, or $3.50 for 10,000 requests.
The calculation uses official standard API rates and identical billable token counts, excluding cache hits and separately charged tools.
For this workload, Haiku reduces the modeled API cost by 90%. If both models achieve acceptable classification accuracy, there is little economic justification for paying 10 times more per token.
Example: Long-Document Analysis
The calculation changes when prompts exceed 100K tokens. Consider a task with 150,000 input tokens and 2,000 output tokens.
GPT-6.1 Terra: 150,000 input tokens cost $0.150, and 2,000 output tokens cost $0.010, for a total of $0.160 per request.
Claude Haiku 5.5: The same token counts cost $0.075 for input and $0.005 for output, for a total of $0.080 per request. The prompt exceeds Haiku’s 100K-token threshold, so its higher long-context rates apply.
These totals use official standard API rates, without caching or separately charged tools.
Haiku remains cheaper, but the savings fall to 50% instead of 90%.
At 300,000 input tokens and 2,000 output tokens, the modeled totals become approximately $0.615 for Terra and $0.155 for Haiku, using the applicable long-context rates.
These examples assume identical billable token counts, no cache hits, and no extra tool charges. Real costs can differ because of tokenization and reasoning-token consumption.
Why Cost per Successful Task Matters More Than Token Pricing
A cheaper model is not necessarily cheaper for the entire workflow.
Suppose Haiku requires several retries to complete a coding task while Terra succeeds on the first attempt. Additional model calls, tool execution, and validation time can eliminate some of Haiku’s pricing advantage.
Conversely, if both models complete a classification request accurately on the first attempt, Haiku’s substantially lower token rates become a clear commercial advantage.
Cost per successful task = Total API and tool costs / Number of successfully completed tasks.
This metric is more meaningful than price per million tokens when selecting models for autonomous agents.
How to Choose
The right choice becomes clearer when the models are evaluated against specific workloads.
Choose GPT-6.1 Terra for demanding software agents. Its stronger independent terminal-coding and automation results make it the better starting candidate for complex coding, multi-step software operations, and autonomous workflows that require recovery after failed actions.
Choose Claude Haiku 5.5 for focused work at scale. Customer-support classification, document summarization, structured extraction, and high-volume subagent tasks are useful starting workloads when the model meets your accuracy requirements. Its lower token prices are most valuable when extra retries or reasoning do not consume the savings.
Test both for professional knowledge work, visual interfaces, and long documents. Haiku leads the cited AA-Briefcase evaluation, while Terra’s visual capabilities make it relevant to screen-based tasks. Neither result establishes a universal winner. Compare evidence retrieval, task completion, and total cost on the actual documents and applications your team uses.
Where Can You Access GPT-6.1 Terra and Claude Haiku 5.5?
You can access GPT-6.1 Terra through the OpenAI API and Claude Haiku 5.5 through the Claude API, or compare them through CometAPI using a shared account. CometAPI offers OpenAI-compatible Chat Completions and Responses formats, plus the Anthropic Messages format for Claude. Choose the endpoint supported by your selected model and workflow; reasoning controls and tool support follow the corresponding interface. Check the model catalog for current availability and billing before routing production traffic.
Conclusion
Start with Terra when demanding automation or terminal coding is the main constraint. Start with Haiku when a focused, high-volume workload meets its quality target at lower cost. For visual tasks and professional knowledge work, test both against representative examples rather than inferring a winner from model positioning.
Should a fallback retry every failed request on the other model?
Use a clear failure signal such as invalid structured output, a failed test, or a rejected tool action. Retry transient network errors separately from quality failures, and cap both retry count and total spend. Before switching models, pass a concise task state and validated tool results so the fallback can continue without repeating actions that already succeeded.
How should a model migration handle existing prompt caches?
Rebuild and measure the cache for the target model rather than assuming a cache entry or token count transfers. Keep stable instructions at the beginning of prompts, then record cache creation, cache reads, and uncached input separately. A workload with many repeated instructions can have a different cost ranking from the uncached examples above.
How can a team evaluate long-context reliability?
Create tasks with known answers and place the supporting evidence at different positions in the document. Include similar-looking distractors and requests that require combining distant passages. Track supported answers, missed evidence, and fabricated references at the context lengths you actually use; advertised context capacity alone does not establish retrieval or reasoning accuracy.
What should an equal-budget Terra versus Haiku test include?
Set a dollar budget per accepted result and include reasoning tokens, retries, tool charges, and validation calls. Let each model use a configuration that fits that budget, then compare success rate and response-time percentiles on the same held-out tasks. This reveals whether extra reasoning on the cheaper model is more useful than a single call to the more expensive one.
Specification
GPT-6.1 Terra
Claude Haiku 5.5
Model ID
gpt-6.1-terra
claude-haiku-5-5
Context window
1,050,000 tokens
1,000,000 tokens
Maximum standard output
128,000 tokens
128,000 tokens
Input → output
Text and images → text
Text and images → text
Reasoning
none, low, medium, high, xhigh, max
Adaptive thinking with effort control
Default effort
Medium
Medium
API availability
OpenAI API; CometAPI
Claude API and cloud platforms; CometAPI
Base input / output per 1M tokens
$1 / $5
$0.10 / $0.50, prompts up to 100K tokens
Architecture and parameter count
No verified architectural comparison established by the cited sources
No verified architectural comparison established by the cited sources
Low API costs:
Benchmark
GPT-6.1 Terra (Xhigh)
Claude Haiku 5.5 (Max)
Winner
Intelligence Index
45
36
Terra
AutomationBench-AA
60%
35%
Terra
Terminal-Bench 4.0
46%
33%
Terra
FrontierCode 2.0
37%
35%
Terra
AA-Briefcase v1.1
1,333
1,576
Haiku
AA-Omniscience v1.1
29
12
Terra
Vendor-reported evaluation
GPT-6.1 Terra
Claude Haiku 5.5
Terminal-Bench 4.0
50.3%
39.2%
OSWorld 2.1
Improvement reported against predecessor
72.4%, offline subset
Difficult-prompt factual error rate
4.3% at maximum effort
Not reported on same test
Chartography
Not reported on same test
46.4%, no tools
Humanity’s Last Exam
Not directly established on same test
45.9%, no tools
Performance metric
GPT-6.1 Terra
Claude Haiku 5.5
Output throughput
91 tokens/s
234 tokens/s
Time to first token
233.43 seconds
326.69 seconds
End-to-end response time (500 tokens)
238.93 seconds
328.83 seconds
Reasoning tokens per Intelligence Index task
39K
173K
Intelligence Index task decoding time (excluding TTFT)
654.90 seconds
598.04 seconds
Pricing component
Terra — official
Terra API in CometAPI
Haiku — official
Haiku API in CometAPI
Base input / 1M tokens
$1.00
$0.80
$0.10
$0.08
Base output / 1M tokens
$5.00
$4.00
$0.50
$0.40
Base cached-input read / 1M tokens
$0.05
Not separately listed
$0.01
$0.008
Long-prompt threshold
More than 272K input tokens
Long-context tier not separately listed
More than 100K prompt tokens
More than 100K prompt tokens
Long-prompt input / 1M tokens
$2.00
Not separately listed
$0.50
$0.40
Long-prompt output / 1M tokens
$7.50
Not separately listed
$2.50
$2.00
Batch discount
50%
Not separately listed
50%
Not separately listed
GPT-6.1 Terra vs Claude Haiku 5.5: Quick Task Selection - CometAPI
