Gemini 3 Deep Think vs GPT-5 (high): The Ultimate Performance & Pricing Comparison

GPT-5 (high) is the safer developer choice because its API identity, capabilities, and evaluation results are documented, while Gemini 3 Deep Think is not verified in Google’s model catalog. Google’s Gemini API model documentation does not list a model named Gemini 3 Deep Think, an API alias, a context window, an output limit, or official benchmark results. OpenAI documents GPT-5 as a reasoning model for coding, reasoning, and agentic tasks in GPT-5 for developers. The data snapshot comes from artificialanalysis.ai, which reports benchmark and pricing fields for GPT-5 but leaves the corresponding Gemini fields unavailable. This comparison therefore measures production readiness and evidence quality, not an unsupported claim that GPT-5 wins every task.

GPT-5 (high) offers a usable selection basis today, while Gemini 3 Deep Think remains an evidence gap that should be treated as an unverified product name. GPT-5 has a stable gpt-5 alias, a fixed snapshot named gpt-5-2025-08-07, and documented support for Chat Completions, Responses, and Batch endpoints in the GPT-5 model documentation. Google’s Gemini API model documentation provides no matching callable entry for Gemini 3 Deep Think. That difference matters more than a nominal price comparison, because an unavailable model cannot be selected, tested, monitored, or given a reliable service-level expectation.

GPT-5 also has a documented 400,000-token context window and a maximum output of 128,000 tokens. It accepts text and image inputs and produces text output, but the GPT-5 model documentation says it does not support audio or video input and output. Its high setting is a reasoning_effort=high parameter, not a separate gpt-5-high model ID, according to GPT-5 for developers.

The practical conclusion is narrow but strong: choose GPT-5 for a build that must start with documented integration behavior. Keep Gemini 3 Deep Think in discovery until Google publishes a callable name and current technical details.

GPT-5 (high) is the only model in this comparison with published task evidence, so it is the only defensible performance choice for a developer evaluation. The data brief reports a 37.8 Artificial Analysis coding index, a 0.846 LiveCodeBench result, and a 0.325757575757576 TerminalBench Hard result for GPT-5. Those values suggest useful coding capability across different task styles, but they do not predict the exact success rate of a private repository, unfamiliar framework, or production incident. The benchmark context also matters: OpenAI reports 74.9% on SWE-bench Verified and 88% on Aider polyglot in GPT-5 for developers, with the Aider evaluation using high reasoning effort. OpenAI also says that 23 of 500 SWE-bench problems were excluded because they could not pass reliably on its infrastructure, so the result should not be read as a universal completion guarantee.

Gemini 3 Deep Think has no comparable verified score in the supplied data. The data brief records null values across its evaluation fields, and Google’s Gemini API model documentation does not identify the model. That prevents a fair head-to-head claim about coding, mathematics, tool use, or agent reliability. The missing evidence is itself a selection risk: developers cannot tell whether the name describes a public API model, a research preview, a product label, or a stale reference.

Speed is also unresolved. The data brief reports 0 median output tokens per second and 0 latency seconds for both entries. Those zeros do not establish a real tie. They indicate that no usable speed measurement is available. Teams with interactive latency requirements should run the same prompts through the actual callable endpoints before committing.

GPT-5 (high) has a real published price, while Gemini 3 Deep Think’s apparent zero price is missing-data evidence rather than a confirmed free tier. The data brief lists GPT-5 at $1.25 per 1M input tokens, $10 per 1M output tokens, and $3.438 per 1M blended tokens using the stated three-to-one mix. The official GPT-5 model documentation also lists cached input at $0.125 per 1M tokens. These prices make output-heavy workflows materially more expensive than prompt-heavy workflows, so a developer should estimate the cost of generated code, tool traces, retries, and long explanations instead of looking only at input pricing.

The data brief lists Gemini 3 Deep Think at $0 for input, output, and blended pricing. Google’s Gemini API pricing documentation does not provide independent pricing for that model, and the model is absent from the official model list. Therefore, the comparison cannot conclude that Gemini is cheaper. A missing price can become a paid endpoint, an unavailable endpoint, or a differently named model with different billing terms.

GPT-5 can still become the more expensive operational choice if high reasoning effort produces long outputs, repeated agent steps, or unnecessary retries. Gemini could become the lower-cost choice if Google confirms a callable model with published prices and comparable task quality, but the supplied evidence does not establish that condition. Cost approval should wait for an official Gemini model ID and price sheet, then use a representative workload rather than a nominal zero.

GPT-5 (high) should be the default pick for production-oriented developers who need a documented API and measurable model behavior. The GPT-5 model documentation identifies the callable gpt-5 alias, documents the 400,000-token context window and 128,000-token maximum output, and states the supported modalities and unsupported fine-tuning capability. GPT-5 for developers documents reasoning_effort settings from minimal through high, verbosity controls, function calling, structured outputs, streaming, and custom tools with grammar-constrained output. Those details give an engineering team something concrete to integrate and test.

GPT-5 is especially suitable for repository debugging, code generation with review, tool-using agents, and tasks where structured responses matter. Its published results include 94.3 on the Artificial Analysis math index, 0.994 on Math 500, and 0.847953216374269 on τ²-bench. These scores do not replace acceptance tests, but they provide a starting signal when no private evaluation exists. A Reddit first-impressions report describes faster small-bug diagnosis, while also reporting shorter and less complete output for full applications. The same source mentions possible hallucinations or incorrect edits in complex existing codebases. These are subjective observations, not controlled evidence, so they justify review gates rather than rejection.

Gemini 3 Deep Think should remain a watchlist candidate until Google confirms its API name, availability, capabilities, limitations, and price. Do not treat the data brief’s zero values as free access or a speed win. Do not claim GPT-5 is universally superior, because Gemini lacks the measurements needed for that conclusion. The evidence supports GPT-5 as the actionable choice and Gemini as an unresolved option.

GPT-5 (high) is easier to approve because the key integration facts are published, while Gemini 3 Deep Think still requires identity and availability verification. The questions below focus on decisions that the supplied materials do not answer directly.