Gemini 3.5 Flash (minimal) vs o3: The Ultimate Performance & Pricing Comparison

Gemini 3.5 Flash (minimal) is the stronger measured default, but its public API identity remains unverified.

The supplied Artificial Analysis snapshot gives Gemini 3.5 Flash (minimal) a 34.9 Intelligence Index, compared with 30.4 for o3. It also reports 248.998 median output tokens per second for Gemini and 128.056 for o3. Both models show 0.3 seconds of latency. Data provided by https://artificialanalysis.ai/

The comparison has an important qualification. Google’s official model directory lists Gemini 3.5 Flash and the stable alias gemini-3.5-flash, but it does not list Gemini 3.5 Flash (minimal) or gemini-3-5-flash-minimal (Google Gemini API models). OpenAI’s current directory likewise does not list o3 (OpenAI models).

For a developer choosing a production model, measured capability and speed favor Gemini. Verifiable procurement and API continuity are unresolved for both names in the current official documentation.

Gemini 3.5 Flash (minimal) offers the better measured balance of intelligence, speed, and blended-token cost.

The Intelligence Index difference is 4.5 points in Gemini’s favor, while the data does not provide a corresponding Gemini Math Index. That makes the overall result directional rather than universal. A coding assistant, agent loop, or interactive generation product may value output speed more than a narrow mathematics score. A symbolic reasoning workflow may need a direct evaluation before accepting Gemini’s broader lead.

The official sources do not validate the exact compared Gemini variant. Google describes the listed gemini-3.5-flash as a model for sustained frontier performance and identifies agent and coding tasks as use cases (Google Gemini API models). That description cannot automatically be transferred to the minimal variant. The same caution applies to o3 because the current OpenAI model directory does not list it (OpenAI models).

Gemini 3.5 Flash (minimal) is the better measured choice for fast response generation, while o3 remains the only model with a supplied math-specific score.

The largest operational difference is output throughput. Artificial Analysis reports 248.998 median output tokens per second for Gemini and 128.056 for o3. That gap matters most after generation begins. It can reduce the time users spend watching a stream, make agent status updates feel more responsive, and shorten long answer delivery. It does not prove that Gemini reaches a correct answer faster for every task, because the snapshot reports the generation metric rather than task-level completion time.

Latency does not separate the models. Both are reported at 0.3 seconds, so Gemini’s throughput advantage should not be mistaken for a faster initial response. Short prompts with short answers may therefore feel similar. Longer streamed responses are more likely to expose the difference.

The intelligence evidence has a second limitation. Gemini scores 34.9 on the supplied Intelligence Index, while o3 scores 30.4. The snapshot also gives o3 an 88.3 Math Index, but supplies no Gemini Math Index. The comparison supports Gemini as the broader measured performer, not as a proven mathematics winner. Developers building numerical reasoning, theorem, optimization, or quantitative analysis features should run matched task tests before switching.

Google’s model documentation does not publish variant-specific context, output, modality, parameter, or benchmark details for the compared minimal name (Google Gemini API models). OpenAI’s current documentation does not publish those details for o3 either (OpenAI models).

Gemini 3.5 Flash (minimal) is slightly cheaper for the supplied blended workload, but o3 is cheaper when generated output dominates the bill.

The data brief reports a blended price of $3.375 per 1M tokens for Gemini versus $3.5 for o3. That makes Gemini the cost leader for the stated 3-to-1 input-to-output mix. Gemini also has the lower input price, at $1.5 per 1M input tokens compared with o3 at $2. For applications that repeatedly send large instructions, documents, conversation history, or tool results, Gemini’s input advantage is the more relevant signal.

The direction reverses for output. Gemini costs $9 per 1M output tokens, while o3 costs $8. Long-form generation, verbose agent traces, code emission, and workflows that repeatedly ask for large structured responses can therefore make o3 cheaper despite its higher blended price.

The blended result should not be treated as a universal production estimate. The right choice depends on the actual input-to-output ratio, caching behavior, retry rate, and response length. The supplied data does not provide those workload distributions, so it cannot identify a single break-even profile beyond the listed comparison.

There is also a procurement risk. Google’s official pricing page lists prices for gemini-3.5-flash, but does not list a separate Gemini 3.5 Flash (minimal) price (Google Gemini API pricing). OpenAI’s current pricing page does not list o3 pricing (OpenAI API pricing). Treat the numerical prices as the supplied benchmark snapshot, then verify live billing identifiers before launch.

Gemini 3.5 Flash (minimal) should be the first candidate for interactive applications, while o3 deserves targeted evaluation for mathematics-heavy workloads.

Choose Gemini first when your product streams substantial answers, processes input-heavy requests, or needs the best measured general score in this comparison. Its 248.998 output tokens per second and $1.5 input price create a strong fit for coding assistants, agent interfaces, document transformation, and user-facing workflows where responsiveness and prompt volume matter. The 34.9 Intelligence Index also gives it the broader measured advantage.

Choose o3 when the central requirement is mathematical reasoning and your own tests confirm that its 88.3 Math Index corresponds to your tasks. The supplied evidence does not establish whether that score predicts performance on your exact problem types. o3 also has the lower output price, at $8 per 1M tokens, which may matter for verbose responses or long generated artifacts.

Do not commit either model to production solely from the names in this comparison. Google’s directory confirms Gemini 3.5 Flash as Stable and shows the alias gemini-3.5-flash, but it does not confirm the minimal variant (Google Gemini API models). OpenAI’s current directory does not list o3 and does not clarify its current endpoint or stable alias (OpenAI models).

The safest selection sequence is: verify that the requested model identifier resolves, run representative prompts, measure quality and completion time, then compare billing under your real token mix. The supplied materials do not provide context windows, output limits, modalities, failure modes, or validated community experience for either exact compared name.

Gemini 3.5 Flash (minimal) is not officially confirmed as a distinct public Google API variant in the supplied documentation.

Google’s model directory lists Gemini 3.5 Flash and gemini-3.5-flash, but it does not list Gemini 3.5 Flash (minimal) or gemini-3-5-flash-minimal (Google Gemini API models). The supplied benchmark snapshot uses the minimal name, so developers should verify the identifier directly before implementation.

O3 is not currently visible in the supplied OpenAI model directory.

The current OpenAI model directory focuses on newer listed models and does not include o3 (OpenAI models). The supplied materials do not confirm whether o3 remains directly callable, has a stable alias, or has been formally replaced. Availability must therefore be checked in the account and API environment used for deployment.

Gemini 3.5 Flash (minimal) is the faster model in the supplied performance data.

Artificial Analysis reports 248.998 median output tokens per second for Gemini and 128.056 for o3, while both models have 0.3 seconds of latency. Gemini should therefore have the clearer advantage for long streamed outputs, but the equal latency means short responses may not feel materially different. Data provided by https://artificialanalysis.ai/

O3 is the safer starting point for math-specific testing, not an automatic mathematics winner.

O3 has the only supplied Math Index result, at 88.3, while no Gemini Math Index is provided. That asymmetry prevents a direct math comparison. Developers should test both models on representative mathematical tasks before making a quality decision.

Gemini 3.5 Flash (minimal) is cheaper for the supplied blended and input pricing comparisons.

The snapshot lists $3.375 versus $3.5 per 1M blended tokens and $1.5 versus $2 per 1M input tokens, with Gemini lower in both comparisons. O3 is cheaper for output at $8 versus Gemini at $9 per 1M output tokens. The final cost depends on the application’s actual token mix, which the supplied materials do not provide.