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Claude Haiku 5.5 vs GPT-5.6 Terra

Compare Claude Haiku 5.5 and GPT-5.6 Terra side-by-side.

Compare Claude Haiku 5.5 vs GPT-5.6 Terra live

Run the same image across every model that supports a task and compare their outputs side-by-side.

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Models in this comparison

Claude Haiku 5.5 vs GPT-5.6 Terra on Vision Evals

Claude Haiku 5.5 scores higher on 5 of the six Vision Evals tasks.

The widest gap is Reasoning, where Claude Haiku 5.5 leads 68.9% to 60.9%.

Overall, Claude Haiku 5.5 averages 77.2% (#20 of 61) against 73.8% (#25 of 61) for GPT-5.6 Terra.

Claude Haiku 5.5 is cheaper ($0.0005 vs $0.0088 per sample), while GPT-5.6 Terra is faster (7.7s vs 13.2s per sample).

Claude Haiku 5.5GPT-5.6 Terra

Claude Haiku 5.5 vs GPT-5.6 Terra Comparison Table

Evals updated October 8, 2026Pricing updated October 8, 2026

PropertyClaude Haiku 5.5GPT-5.6 Terra
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 2026Jul 2026
Context Window1.0M1.1M
ParametersundisclosedUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.100$2.00
Output $/1M$0.500$12.00
Vision Tasks
CaptioningSupportedDemo
Chart Question AnsweringSupportedSupported
ClassificationSupportedDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionSupportedDemo
OCRSupportedDemo
Vision LanguageSupportedSupported
Visual Question AnsweringSupportedDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
77.2%
73.8%
Avg cost / sample$0.0005$0.0088
Avg speed / sample13.23s7.74s
By task
Object Detection (low)
65.8%
±0.6, Mean of 3 runs, range 65.1 to 66.2
$0.0006
60.6%
±0.2, Mean of 3 runs, range 60.3 to 60.7
$0.014
Object Detection (high)
68.2%
±1.0, Mean of 3 runs, range 67.2 to 69.2
$0.0010
61.3%
±0.4, Mean of 3 runs, range 60.8 to 61.6
$0.026
Counting (low)
68.9%
±4.7, Mean of 3 runs, range 64.9 to 74.3
$0.0003
65.8%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0056
Counting (high)
73.0%
±1.3, Mean of 3 runs, range 71.6 to 74.3
$0.0004
62.6%
±2.7, Mean of 3 runs, range 59.5 to 64.9
$0.0076
Identification (low)
83.3%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0002
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0034
Identification (high)
86.5%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0003
86.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0038
OCR (low)
90.1%
±1.1, Mean of 3 runs, range 88.8 to 91.0
$0.0006
89.4%
±0.8, Mean of 3 runs, range 88.8 to 90.3
$0.012
OCR (high)
88.0%
±1.3, Mean of 3 runs, range 87.0 to 89.6
$0.0009
89.4%
±0.6, Mean of 3 runs, range 88.8 to 90.1
$0.023
Data Extraction (low)
85.9%
±1.5, Mean of 3 runs, range 84.5 to 87.6
$0.0002
79.7%
±0.5, Mean of 3 runs, range 79.4 to 80.4
$0.0038
Data Extraction (high)
87.3%
±0.5, Mean of 3 runs, range 86.6 to 87.6
$0.0002
80.4%
±1.0, Mean of 3 runs, range 79.4 to 81.4
$0.0043
Reasoning (low)
68.9%
±2.0, Mean of 3 runs, range 66.9 to 70.9
$0.0004
60.9%
±2.0, Mean of 3 runs, range 59.6 to 63.6
$0.0051
Reasoning (high)
74.8%
±2.6, Mean of 3 runs, range 72.2 to 77.5
$0.0006
65.3%
±1.0, Mean of 3 runs, range 64.2 to 66.2
$0.0067

Claude Haiku 5.5 vs GPT-5.6 Terra: Overview

Claude Haiku 5.5

Claude Haiku 5.5 is a proprietary multimodal language model from Anthropic and the smallest member of the Claude 5.5 family, released on October 7, 2026 after Claude Opus 5.5 and Claude Sonnet 5.5. It accepts text and image input and returns text, with a 1M token context window and up to 128K output tokens per request. It is the first Haiku-class model with an adjustable effort parameter: adaptive thinking is on by default and the model decides how much to reason, steered by effort levels from low to max with medium as the default. Its training data cutoff is June 2026. Pricing starts at $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100K tokens, which Anthropic reports is about 90% lower than Claude Haiku 4.5 for requests in that range.

Anthropic positions Haiku 5.5 for high-volume, latency-sensitive work such as classification, extraction, routing, summarization, and subagent tasks, and describes it as its fastest model to date at standard speed. On visual and agentic evaluations reported at launch, it scores 46.4% on Chartography, a chart reading benchmark, compared with 6.4% for Haiku 4.5 and 61.6% for Sonnet 5.5, and 72.4% on the offline subset of OSWorld 2.1, a screenshot driven computer use benchmark, compared with 15.7% for Haiku 4.5. It uses the same tokenizer as Claude Opus 4.7 and later models, so the same text counts as roughly 30% more tokens than on Haiku 4.5.

GPT-5.6 Terra

GPT-5.6 Terra is the mid-tier reasoning model in OpenAI's GPT-5.6 family, which also includes the flagship Sol and the lightweight Luna. Introduced in a limited preview on June 26, 2026, and made broadly available on July 9, 2026, Terra accepts text and image input and produces text output, supporting vision, function calling, tool use, and agentic workflows. It is designed as a balanced option for everyday professional and production workloads — including coding assistance, document analysis, customer support, and multi-step agent tasks — where both output quality and cost efficiency matter. OpenAI positions Terra as delivering performance competitive with GPT-5.5 at approximately half the price, with a context window of around 1,050,000 tokens. On Terminal-Bench 2.1, Terra scores 84.3%, matching Claude Fable 5 on that benchmark. Under OpenAI's Preparedness Framework, Terra is rated High for cybersecurity and biological capabilities, meaning it demonstrates meaningful capability in those domains without reaching the Critical threshold.

GPT-5.6 introduces a new naming convention in which the generation number (5.6) is paired with a durable capability tier name (Sol, Terra, or Luna), allowing each tier to advance on its own schedule. Terra carries the API identifier gpt-5.6-terra and supports the same reasoning effort controls available across the family, including adjustable reasoning depth. The model includes prompt caching with explicit cache breakpoints and a 30-minute minimum cache life, with cache writes billed at 1.25x the uncached input rate and cache reads receiving a 90% discount. GPT-5.6 Terra is a proprietary, closed-weights model served through the OpenAI API, Codex, and ChatGPT.

Frequently Asked Questions

On Roboflow's Vision Evals, Claude Haiku 5.5 performed better. It scores higher on 5 of the six vision tasks and averages 77.2% (#20 of 61) against 73.8% (#25 of 61) for GPT-5.6 Terra. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Reasoning benchmark at low effort, Claude Haiku 5.5 leads with 68.9% against 60.9%. This is the widest gap between the two models across the benchmark's tasks.

Claude Haiku 5.5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0005 per sample against $0.0088. Claude Haiku 5.5 is priced at $0.10 per 1M input tokens and $0.50 per 1M output; GPT-5.6 Terra is priced at $2.00 per 1M input tokens and $12.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.6 Terra is faster. Across Roboflow's Vision Evals it averaged 7.7s per inference against 13.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.