Gemini 3 Pro Preview (low) vs o3: The Ultimate Performance & Pricing Comparison

Gemini 3 Pro Preview (low) leads the measured general intelligence comparison, but o3 is the safer default for a cost-conscious developer workflow.

The available data puts Gemini 3 Pro Preview (low) at 33.1 on the Artificial Analysis Intelligence Index, compared with 30.4 for o3. The same comparison gives o3 the mathematics edge, at 88.3 versus 86.7. These results describe different strengths rather than a universal winner. Data provided by Artificial Analysis

The larger practical issue is model visibility. Google's current Gemini API model directory does not list Gemini 3 Pro Preview (low), gemini-3-pro-low, or a corresponding API alias. OpenAI's current model directory also does not list o3. Neither provided official source confirms a stable alias, endpoint, or current direct-call status for the compared model.

That uncertainty changes the selection question. If a team needs a documented, supportable production integration, neither model can be approved from the supplied official pages alone. If the team already has verified access and is choosing among measured results, Gemini is the quality-oriented choice, while o3 offers the stronger cost and speed evidence.

Gemini 3 Pro Preview (low) offers the higher general intelligence score, while o3 combines stronger mathematics, lower output pricing, and reported generation speed.

The quality difference is narrow enough that workload composition matters. Gemini's 33.1 Intelligence Index can justify evaluation for broad reasoning tasks, but it does not establish superiority for mathematics, coding, tool use, or multimodal work. The supplied benchmark material does not identify its test method or task coverage in enough detail to map either score directly to a production workload. Data provided by Artificial Analysis

The documentation gap is material. Google's model documentation does not currently provide the compared Gemini model's context window, output limit, parameters, multimodal support, or official positioning. OpenAI's model documentation does not provide those details for o3 either. A developer should therefore treat the benchmark snapshot as a screening signal, not a complete integration recommendation.

o3 has the stronger evidence for fast interactive generation, while Gemini 3 Pro Preview (low) has the broader measured intelligence result.

The performance chart shows a 0.3-second latency value for each model, so the supplied latency metric does not create a meaningful selection advantage. It also shows a reported median output speed of 128.056 tokens per second for o3 and no supplied speed value for Gemini. The missing Gemini value is not evidence that Gemini is slower. It means the comparison cannot establish a speed ranking. Data provided by Artificial Analysis

For an interactive coding assistant, o3's reported speed makes it easier to form a testable latency expectation. That advantage remains conditional because the supplied material does not describe hardware, streaming configuration, prompt length, output length, concurrency, or measurement method. A production team should reproduce its own representative requests before treating the speed result as an SLA input.

Gemini's 33.1 Intelligence Index versus o3's 30.4 suggests a potential advantage on the benchmark's wider intelligence construct. The result does not explain whether the difference comes from planning, instruction following, coding, factuality, or another component. The supplied research also contains no reliable community reports describing coding behavior, speed perception, or failure patterns for either target model. Google's model directory and OpenAI's model directory do not fill that gap with model-specific benchmark or limitation details.

o3 is the lower-cost option for the supplied blended mix, but workload shape determines whether that advantage survives in production.

The blended comparison prices o3 at $3.5 per 1M tokens and Gemini 3 Pro Preview (low) at $4.5. Input pricing is tied at $2 per 1M tokens, while output pricing favors o3 at $8 versus Gemini at $12. The chart already captures those prices, so the important interpretation is where token consumption occurs. Data provided by Artificial Analysis

Input-heavy applications have no listed price separation. Output-heavy applications give o3 a clearer economic advantage because generated tokens carry the larger listed difference. Gemini could still become the cheaper operational choice if it solves tasks with fewer retries, shorter answers, or fewer fallback calls, but the supplied material provides no usage, retry, or failure-rate data to support that claim.

The official pricing pages do not resolve the availability problem. Google's Gemini API pricing page does not list Gemini 3 Pro Preview (low) or a matching alias. OpenAI's API pricing page does not list o3. The pages describe broader pricing structures, but those general rules cannot be converted into a confirmed current price for an unlisted target model.

For budgeting, use the Artificial Analysis snapshot as a comparison reference, then verify the actual billable model identifier and current provider price before committing traffic.

o3 is the default recommendation for cost-sensitive interactive development, while Gemini 3 Pro Preview (low) deserves a controlled trial for broad reasoning quality.

Choose Gemini 3 Pro Preview (low) when your evaluation shows that its 33.1 Intelligence Index advantage translates into better results on your own planning, synthesis, or general reasoning tasks. That recommendation requires verified access because Google's current Gemini API model directory does not list the model or a confirmed API alias. The supplied sources also do not document its context window, output limit, parameters, multimodal support, or official capability boundaries.

Choose o3 when mathematics, output-heavy workloads, or responsive generation dominate your requirements. Its 88.3 Math Index leads Gemini's 86.7, its output price is $8 rather than $12 per 1M tokens, and it has the only reported output-speed value, 128.056 tokens per second. Those advantages are measurable, but OpenAI's current model directory and pricing page do not currently confirm o3's active endpoint, stable alias, or listed price.

Do not select either model solely from the names, release dates, or benchmark scores. The supplied data lists Gemini's release date as 2025-11-18 and o3's as 2025-04-16, but the official pages do not establish a supported migration path or replacement relationship. Data provided by Artificial Analysis

A sensible approval gate is: verify the exact model identifier, run representative prompts, measure quality and retries, then compare billed input and output usage. The supplied evidence is insufficient to predict tool calling, multimodal behavior, context handling, or failure modes for either model.

o3 is the more defensible starting point when a developer must prioritize measurable cost, mathematics, and generation-speed evidence.

The supplied research does not confirm the production status, stable API alias, or documented limits of either compared model. Google's model directory omits Gemini 3 Pro Preview (low), while OpenAI's model directory omits o3. Data provided by Artificial Analysis

The unresolved documentation means a benchmark lead cannot replace an integration check. Teams should validate access and behavior in their own environment before making a provider-level commitment.