Gemma 3 4B vs GPT-5.6 Terra
Compare Gemma 3 4B and GPT-5.6 Terra side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Gemma 3 4B vs GPT-5.6 Terra: Overview
Gemma 3 4B, released on March 12, 2025, is the mid-sized member of Google DeepMind’s open-weight Gemma 3 family. With about 4 billion parameters, it is multimodal—supporting text and image inputs and generating text outputs. Like the larger Gemma 3 models, it features a 128,000-token input context window with an output capacity of ~8,192 tokens, enabling it to handle long documents and mixed text–image reasoning tasks.
The 4B variant is designed as a balance between efficiency and capability: it offers multilingual support across 140+ languages, strong summarization and reasoning performance, and compatibility with moderate hardware. Inference can run with ~6.4 GB VRAM in BF16, or significantly less in quantized 8-bit (~4.4 GB) or 4-bit (~3.4 GB) modes, making it accessible to developers outside large-scale infrastructure. While it lags behind the 12B and 27B versions on the most complex reasoning and multimodal benchmarks, its lower compute footprint makes it ideal for research, prototyping, and practical deployment where efficiency matters.
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.
Gemma 3 4B vs GPT-5.6 Terra Comparison Table
| Property | Gemma 3 4B | GPT-5.6 Terra |
|---|---|---|
| Organization | OpenAI | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2025 | Jul 2026 |
| Context Window | 128K | 1.1M |
| Parameters | 4B | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.050 | $2.50 |
| Output $/1M | $0.100 | $15.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| classification | Demo | |
| Document Question Answering | ||
| object-detection | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 37.31% | |
| Avg Response Time | 16.80s | |
| Defect Detection | 60%(9/15) | |
| Document Understanding | 55.6%(5/9) | |
| Object Counting | 0%(0/10) | |
| Object Understanding | 42.9%(6/14) | |
| Spatial Understanding | 26.3%(5/19) | |
| OCR | ||
| Overall Score | 64.19% | |
| Avg Response Time | 0.92s | |
| Median input tokensincl. image tokens | 300 | |
| Median output tokens | 12 | |
| Est. cost / taskon this benchmark | <$0.0001 | |
| Focused Scene OCR | 63.6%(63/99) | |
| Handwritten Math | 10%(1/10) | |
| License Plate Recognition | 86.7%(26/30) | |
| Text Recognition | 73.3%(22/30) | |
| VQA & Extraction | 58.3%(35/60) | |