Gemini 3 Pro vs GPT 5.1: which is better? A Complete Comparison

Both OpenAI’s GPT-5.1 and Google’s Gemini 3 Pro represent incremental but meaningful steps in the ongoing arms race for general-purpose, multimodal AI. GPT-5.1 is a refinement of the GPT-5 line — focusing on adaptive reasoning, lower latency for simple tasks, and stylistic/personality controls for more natural conversational tone. Google’s Gemini 3 Pro pushes the frontier on multimodality, deep reasoning modes, and tight tooling for agentic workflows.

GPT-5.1 (OpenAI) and Gemini 3 Pro Preview (Google/DeepMind) target overlapping but distinct tradeoffs: GPT-5.1 focuses on faster adaptive reasoning, developer workflows and coding reliability with new agent/coding tools and token/cost optimizations; Gemini 3 Pro doubles down on extreme multimodal scale (video/audio/images + very large context windows) and deep integration into Google’s products and developer stack.

Which is “better” depends on your use case: long-document/multimodal agent workloads → Gemini 3 Pro; code-first, tool-centric agent workflows with fine developer controls → GPT-5.1. Below I justify that with numbers, benchmarks, costs and runnable examples.

What is GPT-5.1 and what are its headline features?

Overview and positioning

GPT-5.1 is OpenAI’s incremental upgrade to the GPT-5 family, released in November 2025. It’s presented as a “faster, more conversational” evolution of GPT-5 with two prominent variants (Instant and Thinking) and developer-focused additions such as extended prompt caching, new coding tools (apply_patch, shell), and improved adaptive reasoning that dynamically adjusts “thinking” effort to task complexity. These features are designed to make agentic and coding workflows more efficient and predictable.

Key features (vendor claims)

• Two variants: GPT-5.1 Instant (more conversational, faster for usual prompts) and GPT-5.1 Thinking (allocates more internal “thinking” time for complex, multi-step tasks).
• Adaptive reasoning: the model dynamically decides how much “thinking” to spend on a query; the API exposes reasoning_effort (values like 'none', 'low', 'medium', 'high') so developers can trade latency vs reliability. GPT-5.1 defaults to 'none' (fast) but can be asked to increase effort for complex tasks. Example: a simple npm list answer went from ~10s (GPT-5) to ~2s (GPT-5.1) in OpenAI’s examples.
• Multimodal: GPT-5.1 continues GPT-5’s broad multimodal abilities (text + images + audio + video in ChatGPT workflows) with tighter integration into tool-based agents (e.g., browsing, function calls).
• Coding improvements — OpenAI reports SWE-bench Verified: 76.3% (GPT-5.1 high) vs 72.8% (GPT-5 high), and other wins on code-editing benchmarks.
• New tools for safe agentic work — apply_patch (structured diffs for code edits) and a shell tool (propose commands; integration executes and returns outputs). These enable iterative, programmatic code editing and controlled system interrogation by the model.

What is Gemini 3 Pro Preview and what are its headline features?

Gemini 3 Pro Preview is Google/DeepMind’s latest frontier model (preview launched November 2025). Google positions it as an ultra-capable multimodal reasoning model with enormous context capacity, deep product integration (Search, Gemini app, Google Workspace), and a focus on “agentic” workflows (Antigravity IDE, agent artifacts, etc.). The model is explicitly built to handle text, images, audio, video and entire code repositories at scale.

Key capabilities

• Ultra-large context window: Gemini 3 Pro supports up to 1,000,000 tokens of context (input) and up to 64K tokens of text output in many published docs — this is a qualitative leap for use cases like ingesting multi-hour video transcripts, codebases, or long legal documents.
• Multimodal depth: State-of-the-art performance on multimodal benchmarks (image/video understanding, MMMU-Pro,e.g., 81% MMMU-Pro, 87.6% Video-MMMU, high GPQA and scientific reasoning scores), with specialized handling for image/video frame tokenization and video frame budgets in the API docs; first-class inputs: text, images, audio, video in one prompt.
• Developer tooling & agents: Google launched Antigravity (agent-first IDE), Gemini CLI updates, and integration across Vertex AI, GitHub Copilot preview, and AI Studio — signaling strong support for agentic developer workflows. Artifacts, orchestrated agents, and agent logging features are unique product additions.

Gemini 3 Pro vs GPT-5.1 — quick comparison table

How do they compare — architecture & capabilities?

Architecture: dense reasoning vs sparse MoE

OpenAI (GPT-5.1): OpenAI emphasizes training changes that enable adaptive reasoning (spend more or less compute per token depending on difficulty) rather than publishing raw parameter numbers. OpenAI focuses on the reasoning policy and tooling that make the model act agentically in a reliable way.

Gemini 3 Pro: sparse MoE techniques and model engineering that allow very large capacity with sparse activation at inference — one explanation for how Gemini 3 Pro can be scaled to handle 1M token context while remaining practical. Sparse MoE excels when you need very large capacity for varied tasks but want to reduce average inference cost.

Model philosophy and “thinking”

OpenAI (GPT-5.1): Emphasizes adaptive reasoning where the model privately decides when to spend more compute cycles to think harder before answering. The release also splits models into conversational vs. thinking variants to let the system match user needs automatically. This is a “two-track” approach: keep common tasks snappy while allocating extra effort to complex tasks.

Google (Gemini 3 Pro): Emphasizes deep reasoning + multimodal grounding with explicit support for “thinking” processes inside the model and a tool ecosystem that includes structured tool outputs, search grounding, and code execution. Google’s messaging is that the model itself plus the tooling is tuned to produce reliable step-by-step solutions at scale.

Takeaway: philosophically they converge — both offer “thinking” behavior — but OpenAI emphasizes variant-driven UX + caching for multi-turn workflows, while Google emphasizes a tightly integrated multimodal + agentic stack and shows benchmark numbers to back the claim.

Context windows and I/O limits (practical effect)

• Gemini 3 Pro: input 1,048,576 tokens, output 65,536 tokens (Vertex AI model card). This is the clearest advantage when working with very large documents.
• GPT-5.1:GPT-5.1 Thinking in ChatGPT has a context limit of 196k tokens (release notes) for that variant; other GPT-5 variants may have different limits — OpenAI emphasize caching and “reasoning_effort” rather than pushing to 1M tokens at the moment.

Takeaway: if you need to load an entire large repository or a long book into a single prompt, Gemini 3 Pro’s published 1M window is a clear advantage in the preview. OpenAI’s extended prompt caching addresses continuity across sessions rather than a single giant context in the same way.

Tooling, agent frameworks and ecosystem

• OpenAI: apply_patch + shell + other tools focused on code editing and safe iteration; strong ecosystem integrations (third-party coding assistants, VS Code extensions, etc.).
• Google: Gemini’s SDKs, structured outputs, built-in grounding with Google Search, code execution, and Antigravity (an IDE and manager for multiple agents) make for a highly agentic, multi-agent orchestration story. Google also exposes grounded search and built-in verifier style artifacts for agent transparency.

Takeaway: both have first-class agent support. Google’s approach bundles the agent orchestration into product features (Antigravity, Search grounding) more visibly; OpenAI focuses on developer tool primitives and caching to enable similar flows.

What do benchmarks say — who’s faster, more accurate?

Benchmarks & performance

Gemini 3 Pro leads on multimodal, visual, and long-context reasoning, while GPT-5.1 remains extremely competitive on coding (SWE-bench) and emphasizes faster/adaptive reasoning for simple textual tasks.

Gemini 3 Pro advantages:

• Large gains on multimodal and visual reasoning tests (ARC-AGI-2, MMMU-Pro). This matches Google’s emphasis on native multimodality and a very large context window.
• Strong long-context retrieval/recall (MRCR v2 / 128k) and top scores on some algorithmic coding Elo benchmarks.

GPT-5.1 advantages“

• Coding / engineering workflows: GPT-5.1 advertises adaptive reasoning and speed improvements (faster for simple tasks, more measured thinking for hard tasks) and is essentially tied or slightly ahead on SWE-Bench Verified in published numbers (76.3% reported). OpenAI emphasizes latency/efficiency improvements (adaptive reasoning, prompt caching).
• GPT-5.1 is positioned for lower latency / developer ergonomics in many chat/code workflows (OpenAI docs highlight extended prompt caching and adaptive reasoning).

Latency / throughput tradeoffs

• GPT-5.1 is optimized for latency on simple tasks (Instant) while scaling up thinking budgets on difficult tasks — this can reduce token bills and perceived latency for many apps.
• Gemini 3 Pro is optimized for throughput and multimodal context — it may be less focused on micro-latency improvements for trivial queries when used at extreme context sizes, but it is designed to handle massive inputs in one shot.

Takeaway: based on vendor-published numbers and early third-party reports, **Gemini 3 Pro currently claims superior raw benchmark scores across many standardized multimodaltasks**, while *GPT-5.1 focuses on refined behavior, developer tooling and session continuity* — they’re optimized for overlapping but slightly different developer workflows.

How do their multimodal capabilities compare?

Input types supported

• GPT-5.1: Supports text, images, audio and video inputs inside ChatGPT and API workflows; GPT-5.1’s innovation is more about how it combines adaptive reasoning and tool use with multimodal inputs (e.g., better patch/apply semantics when editing code that’s linked to a screenshot or video). That makes GPT-5.1 compelling where reasoning + tool autonomy + multimodality are required.
• Gemini 3 Pro: Designed as a multimodal reasoning engine that can take text, images, video, audio, PDFs and code repositories — and it publishes Video-MMMU and other multimodal benchmark numbers to support the claim. Google emphasizes video and screen understanding improvements (ScreenSpot-Pro).

Practical differences

• Video understanding: Google published explicit Video-MMMU numbers and shows noticeable improvements; if your product ingests long video or screen recordings for reasoning/agents, Gemini emphasizes that capability.
• Agentic multimodality (screen + tools): Gemini’s ScreenSpot-Pro improvements and Antigravity agent orchestration are pitched for flows where multiple agents interact with a live IDE, browser, and local tools. OpenAI addresses agentic workflows primarily via tools (apply_patch, shell) and caching but without a packaged multi-agent IDE.

Takeaway: both are strong multimodal models; Gemini 3 Pro’s published numbers show it as the leader on several multimodal benchmarks, especially video and screen understanding. GPT-5.1 is still a broadly multimodal model and emphasizes developer integration, safety and interactive agent flows.

How do API access and pricing compare?

API models & names

• OpenAI: gpt-5.1, gpt-5.1-chat-latest, gpt-5.1-codex, gpt-5.1-codex-mini. Tools and reasoning parameters are available in the Responses API (tools array, reasoning_effort, prompt_cache_retention).
• Google / Gemini: accessible via Gemini API / Vertex AI (gemini-3-pro-preview on the Gemini models page) and via the new Google Gen AI SDKs (Python/JS) and Firebase AI Logic.

Pricing

• GPT-5.1 (OpenAI official): Input $1.25 / 1M tokens; Cached input $0.125 / 1M; Output $10.00 / 1M tokens. (Frontier pricing table.) ([OpenAI][2])
• Gemini 3 Pro Preview (Google): Standard paid tier example: Input $2.00 / 1M tokens (≤200k) or $4.00 / 1M tokens (>200k); Output $12.00 / 1M tokens (≤200k) or $18.00 / 1M tokens (>200k).

CometAPI is a third-party platform that aggregates models from various vendors and has now integrated Gemini 3 Pro Preview API and GPT-5.1 API, Furthermore, the integrated API is priced at 20% of the official price:

Cost implication: for high-volume, but small-context token workloads (short prompts, small responses), OpenAI’s GPT-5.1 is generally cheaper per output token than Gemini 3 Pro Preview. For very large context workloads (ingesting many tokens), Gemini’s batch / free tier / long-context economics and product integrations may make sense — but do the math on your token volumes and grounding calls.

Which is better for which use cases?

Choose GPT-5.1 if:

• You value developer tooling primitives (apply_patch/shell) and tight integration into existing OpenAI agent workflows (ChatGPT, Atlas browser, agent mode). GPT-5.1’s variants and adaptive reasoning are tuned for conversational UX and developer productivity.
• You want extended prompt caching across sessions to reduce cost/latency in multi-turn agents.
• You need the OpenAI ecosystem (existing fine-tuned models, ChatGPT integrations, Azure/OpenAI partnerships).

Choose Gemini 3 Pro Preview if:

• You need very large single-prompt context handling (1M tokens) to load entire codebases, legal documents, or multi-file datasets into one session.
• Your workload is video + screen + multimodal heavy (video understanding / screen parsing / agentic IDE interactions) and you want the model that vendor tests currently show leading those benchmarks.
• You prefer Google-centric integration (Vertex AI, Google Search grounding, Antigravity agent IDE).

Conclusion

Both GPT-5.1 and Gemini 3 Pro are cutting-edge, but they emphasize different tradeoffs: GPT-5.1 focuses on adaptive reasoning, coding reliability, developer tools and cost-efficient outputs; Gemini 3 Pro focuses on scale (1M token context), native multimodality and deep product grounding. Decide by matching their strengths to your workload: long, multimodal, single-shot ingestion → Gemini; iterative code/agent workflows, cheaper per-token generation for outputs → GPT-5.1.

Developers can access Gemini 3 Pro Preview API and GPT-5.1 API through CometAPI. To begin, explore the model capabilities of CometAPI in the Playground and consult the Continue API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.

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