Gemini 4 Carbon vs GPT-5.6 Terra
Gemini 4 Carbon and GPT-5.6 Terra score 89.4% vs 66.0% averaged across five Vision Evals tasks (#1 and #31 of 68 models), so Gemini 4 Carbon leads overall.
- Gemini 4 Carbon wins all 5 tasks, with its widest lead on OCR (87.2% vs 56.3%).
- Gemini 4 Carbon is about half the cost: $0.0061 vs $0.010 overall average cost per sample.
- Gemini 4 Carbon is faster: 8.7s vs 9.5s per sample.
Roboflow Vision Evals. Full breakdown below.
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Gemini 4 Carbon vs GPT-5.6 Terra on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Gemini 4 Carbon vs GPT-5.6 Terra Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemini 4 Carbon | GPT-5.6 Terra |
|---|---|---|
| Organization | OpenAI | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 2026 | Jul 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $2.00 |
| Output $/1M | No published price | $12.00 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Demo | Not listed |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 89.4% | 66.0% |
| Avg cost / sample | $0.0061 | $0.010 |
| Avg speed / sample | 8.71s | 9.52s |
| By task | ||
| Object Detection (low) | 84.5% ±0.3, Mean of 3 runs, range 84.2 to 84.8 | 60.6% ±0.2, Mean of 3 runs, range 60.3 to 60.7 |
| Object Detection (high) | 85.9% ±0.3, Mean of 3 runs, range 85.6 to 86.2 | 61.3% ±0.4, Mean of 3 runs, range 60.8 to 61.6 |
| Counting (low) | 90.1% ±1.4, Mean of 3 runs, range 89.2 to 91.9 | 65.8% ±2.0, Mean of 3 runs, range 63.5 to 67.6 |
| Counting (high) | 86.9% ±2.7, Mean of 3 runs, range 83.8 to 89.2 | 62.6% ±2.7, Mean of 3 runs, range 59.5 to 64.9 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 86.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 87.2% | 56.3% |
| by category |
|
|
| OCR (high) | 89.7% | 56.9% |
| by category |
|
|
| Reasoning (low) | 91.4% ±1.7, Mean of 3 runs, range 90.1 to 93.4 | 60.9% ±2.0, Mean of 3 runs, range 59.6 to 63.6 |
| Reasoning (high) | 92.3% ±0.3, Mean of 3 runs, range 92.0 to 92.7 | 65.3% ±1.0, Mean of 3 runs, range 64.2 to 66.2 |
Gemini 4 Carbon vs GPT-5.6 Terra: Overview
Gemini 4 Carbon is a multimodal model from Google. It builds on Gemini 4 Argon and supports reasoning over text and images. Google describes improvements in coding and long-running tasks. Model weights are not publicly released.
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. 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, Gemini 4 Carbon performed better. It scores higher on all five vision tasks and averages 89.4% (#1 of 68) against 66.0% (#31 of 68) 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 OCR benchmark at low effort, Gemini 4 Carbon leads with 87.2% against 56.3%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 4 Carbon is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0061 per sample against $0.010. Actual costs depend on your image sizes, prompts, and output length.
Gemini 4 Carbon is faster. Across Roboflow's Vision Evals it averaged 8.7s per inference against 9.5s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.