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 5.5 vs GPT-5.6 Terra Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Claude Haiku 5.5 | GPT-5.6 Terra |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Jul 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | undisclosed | Unknown |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $2.00 |
| Output $/1M | $0.500 | $12.00 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Supported | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Supported | Demo |
| OCR | Supported | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Supported | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
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 / sample | 13.23s | 7.74s |
| By task | ||
| Object Detection (low) | 65.8% ±0.6, Mean of 3 runs, range 65.1 to 66.2 | 60.6% ±0.2, Mean of 3 runs, range 60.3 to 60.7 |
| Object Detection (high) | 68.2% ±1.0, Mean of 3 runs, range 67.2 to 69.2 | 61.3% ±0.4, Mean of 3 runs, range 60.8 to 61.6 |
| Counting (low) | 68.9% ±4.7, Mean of 3 runs, range 64.9 to 74.3 | 65.8% ±2.0, Mean of 3 runs, range 63.5 to 67.6 |
| Counting (high) | 73.0% ±1.3, Mean of 3 runs, range 71.6 to 74.3 | 62.6% ±2.7, Mean of 3 runs, range 59.5 to 64.9 |
| Identification (low) | 83.3% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| Identification (high) | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | 86.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 90.1% ±1.1, Mean of 3 runs, range 88.8 to 91.0 | 89.4% ±0.8, Mean of 3 runs, range 88.8 to 90.3 |
| OCR (high) | 88.0% ±1.3, Mean of 3 runs, range 87.0 to 89.6 | 89.4% ±0.6, Mean of 3 runs, range 88.8 to 90.1 |
| Data Extraction (low) | 85.9% ±1.5, Mean of 3 runs, range 84.5 to 87.6 | 79.7% ±0.5, Mean of 3 runs, range 79.4 to 80.4 |
| Data Extraction (high) | 87.3% ±0.5, Mean of 3 runs, range 86.6 to 87.6 | 80.4% ±1.0, Mean of 3 runs, range 79.4 to 81.4 |
| Reasoning (low) | 68.9% ±2.0, Mean of 3 runs, range 66.9 to 70.9 | 60.9% ±2.0, Mean of 3 runs, range 59.6 to 63.6 |
| Reasoning (high) | 74.8% ±2.6, Mean of 3 runs, range 72.2 to 77.5 | 65.3% ±1.0, Mean of 3 runs, range 64.2 to 66.2 |
Claude Haiku 5.5 vs GPT-5.6 Terra: Overview
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 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.