Gemini 3.1 Pro Preview vs o3: The Ultimate Performance & Pricing Comparison

Gemini 3.1 Pro Preview is the stronger measured general-purpose choice, while o3 remains the cheaper option for output-heavy workloads. The Artificial Analysis Intelligence Index scores Gemini 3.1 Pro Preview at 46.5 and o3 at 30.4. The same dataset reports 129.625 median output tokens per second for Gemini 3.1 Pro Preview and 128.056 for o3, with both models at 0.3 seconds of latency. Data provided by https://artificialanalysis.ai/

That result does not establish a universal winner. The dataset includes a coding score of 68.8 for Gemini 3.1 Pro Preview, but no comparable o3 coding value. It includes a math score of 88.3 for o3, but no comparable Gemini 3.1 Pro value. Developers therefore have one broad signal favoring Gemini, plus narrow signals that cannot be compared symmetrically.

Google describes Gemini 3.1 Pro as a Preview model for advanced intelligence, complex problem solving, agentic coding, and vibe coding, and lists the API alias as gemini-3.1-pro-preview (Google Gemini API Models). OpenAI's current model directory does not list o3 among the models shown in the supplied material (OpenAI Models). This visibility gap matters as much as benchmark performance for a new production integration.

Gemini 3.1 Pro Preview offers the better measured intelligence result, but o3 offers the clearer cost advantage and a distinct math signal. The practical choice depends on whether your application is broad and agentic, output-cost sensitive, or dependent on mathematical reasoning.

Gemini is the better default for teams seeking a broader measured capability signal and an available coding result. o3 is the better default for applications that generate many tokens and can validate its mathematical behavior independently.

The documentation creates a second selection constraint. Google currently lists Gemini 3.1 Pro Preview in its model catalog (Google Gemini API Models). The supplied OpenAI model documentation instead presents a current product line centered on GPT-5.6 models and does not list o3 (OpenAI Models). That does not prove o3 is unusable, but it means developers should verify access before committing architecture or migration work.

Gemini 3.1 Pro Preview has the stronger directly comparable intelligence result, but the available evidence does not prove superiority on every developer workload. Its Intelligence Index is 46.5, compared with 30.4 for o3, a difference of 16.1 points in the supplied comparison. For a developer, that gap is most relevant when the application combines planning, instruction following, synthesis, and multi-step reasoning rather than performing one narrow mathematical operation.

The coding evidence points in the same direction for Gemini, but it is incomplete. Gemini 3.1 Pro Preview has a Coding Index of 68.8 in the data brief. No o3 coding score is provided, so the result cannot answer whether Gemini is better for code generation, repository edits, debugging, or tool-driven implementation. Google’s official description mentions agentic coding and vibe coding, but the supplied official page does not provide independent benchmark results or detailed capability boundaries (Google Gemini API Models).

o3 has the only supplied Math Index, at 88.3. That makes o3 a serious candidate for math-heavy workflows, but it does not establish a comparative win because Gemini’s corresponding value is absent. Teams building symbolic reasoning, quantitative analysis, or verification features should run the same test set on both models.

Speed is unlikely to decide the choice. Gemini reports 129.625 median output tokens per second, while o3 reports 128.056, and both show 0.3 seconds of latency. That small measured separation may disappear once prompts, tool calls, retries, streaming behavior, and network conditions enter the system. The brief contains no reliable community evidence about coding feel, speed perception, model habits, or failure cases. Those areas remain evidence gaps, not hidden advantages.

o3 is cheaper in the supplied blended-cost comparison, but Gemini 3.1 Pro Preview can be economically preferable when better task completion reduces retries, reviews, or human intervention. The data brief reports $3.5 per 1M blended tokens for o3 versus $4.500000000000001 for Gemini 3.1 Pro Preview. It also reports equal input pricing at $2, while output pricing favors o3 at $8 versus $12.

The price difference matters most for applications that generate long answers, repeated code patches, or tool-use traces. A system that sends large prompts but produces short responses sees the same listed input price for both models. A system that produces substantial output sees o3’s lower output price more directly. However, token price is only one part of application cost. If Gemini’s measured intelligence advantage improves first-pass success, the additional model spend could be offset by fewer retries or less manual correction. The supplied materials do not measure task completion, retry rates, review time, or total cost per successful result, so that economic crossover cannot be calculated from the evidence.

Official pricing visibility is also limited. The supplied Google pricing material explains that paid plans provide higher limits, context caching, Batch API access, and advanced model access, with Batch API described as offering a 50% cost discount, but it does not provide a specific Gemini 3.1 Pro price (Google Gemini API Pricing). The supplied OpenAI pricing page does not list o3 pricing (OpenAI API Pricing). Treat the data brief as the comparison basis, then confirm current account-level pricing and availability before procurement.

Gemini 3.1 Pro Preview is the safer capability-first pick for broad developer agents, while o3 is the safer cost-first pick for output-heavy or math-focused experiments. Gemini’s broad Intelligence Index is 46.5, and its available Coding Index is 68.8. Those values support a starting hypothesis for repository assistance, planning, synthesis, and multi-step coding tasks, but not a complete production guarantee.

Choose Gemini 3.1 Pro Preview when the application must combine several forms of reasoning in one interaction. Examples include an agent that reads project context, proposes an implementation, edits files, explains tradeoffs, and checks its own work. The model’s official positioning includes complex problem solving and agentic coding, while the data brief supplies the stronger directly comparable intelligence score (Google Gemini API Models). Keep the Preview label in the risk assessment. The supplied official material does not specify stability commitments, context limits, output limits, multimodal boundaries, rate limits, or tool-calling constraints.

Choose o3 when generated-token cost is a primary constraint, especially if the workload can tolerate a less favorable broad intelligence score. Its blended price is $3.5 per 1M tokens, and its output price is $8 per 1M tokens. o3 also has the available Math Index of 88.3, which justifies a focused evaluation for mathematical or quantitative tasks. The evidence does not show whether o3 remains directly callable, has a stable alias, or has been formally replaced. The supplied OpenAI model page does not list it, so access verification is mandatory (OpenAI Models).

For a production decision, test both models on the same representative prompt set. Measure successful task completion, correction count, tool-call validity, output length, and human review time. The supplied materials do not contain those measurements, so no responsible comparison can fill them in by inference.

Gemini 3.1 Pro Preview and o3 require an availability check before a team treats benchmark and price data as an integration decision. Google exposes Gemini 3.1 Pro Preview in the supplied model catalog, while the supplied OpenAI catalog does not show o3 (Google Gemini API Models, OpenAI Models). The difference may reflect catalog status rather than runtime behavior, and the supplied sources do not resolve that uncertainty.

The most important missing evidence concerns production boundaries. Neither supplied model source provides a complete, directly comparable account of context size, maximum output, supported parameters, multimodal input, rate limits, tool-calling behavior, or failure modes. Community evidence is also unavailable in the brief. Developers should therefore treat the measured metrics as selection inputs, not as a substitute for a task-specific acceptance test.

Pricing needs the same discipline. The data brief provides comparative token prices, but the supplied official pages do not confirm current model-specific pricing for either model. Google documents general paid-plan capabilities and a 50% Batch API discount, while OpenAI’s supplied pricing page does not list o3 (Google Gemini API Pricing, OpenAI API Pricing). Confirm the actual billing mode, limits, and endpoint access in the account that will run the workload.