Gemini 3.5 Flash (medium) vs o3: Which Model Should Developers Choose?

Gemini 3.5 Flash (medium) is the stronger default for general developer workloads, but o3 remains the more defensible choice for mathematics-heavy reasoning.

The comparison is unusually dependent on evidence quality. The data snapshot gives Gemini 3.5 Flash (medium) an Artificial Analysis Intelligence Index of 45.4, compared with 30.4 for o3. It also gives Gemini a median output speed of 276.619 tokens per second, compared with 128.056 for o3. Latency is tied at 0.3 seconds. Data provided by https://artificialanalysis.ai/

That performance advantage does not settle every engineering decision. The snapshot gives o3 a Math Index of 88.3, while no corresponding Gemini mathematics score is provided. Neither model has a context-window value in the supplied data. Developers therefore have a meaningful general-intelligence and throughput signal, but not a complete task-specific evaluation.

Model identity is another risk. Google lists gemini-3.5-flash as Stable, yet its official model documentation does not separately list Gemini 3.5 Flash (medium) or gemini-3-5-flash-medium. Google’s model documentation supports the official alias, but not the exact comparison label.

Gemini 3.5 Flash (medium) offers the better broad-use profile, while o3 has the clearer evidence for specialized mathematical work.

Decision area	Better-supported choice	Why it matters
General intelligence	Gemini 3.5 Flash (medium)	Its Intelligence Index is 45.4 versus 30.4 for o3, a 15-point gap in the supplied evaluation.
Mathematical reasoning	o3	o3 has a Math Index of 88.3; the snapshot supplies no Gemini Math Index.
Interactive generation	Gemini 3.5 Flash (medium)	Its median output speed is 276.619 tokens per second versus 128.056 for o3.
Initial response latency	Tie	Both models show 0.3 seconds in the supplied snapshot.
Blended token cost	Gemini 3.5 Flash (medium)	The listed blended price is $3.375 versus $3.5 per 1M tokens.
Output-heavy workloads	o3	Output costs are $8 per 1M tokens for o3 versus $9 for Gemini.
Verified public availability	Gemini 3.5 Flash (medium)	Google’s documentation lists gemini-3.5-flash as Stable. OpenAI’s supplied current directory does not list o3.

The availability comparison is not symmetrical. Google documents a Stable gemini-3.5-flash endpoint, but does not validate the medium suffix as a separate public model. Google’s model documentation also lists a newer Gemini 3.6 Flash without stating that Gemini 3.5 Flash has been replaced.

OpenAI’s supplied model directory centers on GPT-5.6 Sol, GPT-5.6 Terra, GPT-5.6 Luna, and other listed models, without listing o3. OpenAI’s model directory does not establish whether o3 remains callable, has a stable alias, or has an official successor. That makes current access a deployment question rather than a settled product fact.

Gemini 3.5 Flash (medium) should feel substantially more responsive during long generations, while o3’s mathematical specialization may outweigh that advantage for narrower tasks.

The output-speed gap is large enough to affect product behavior. Gemini’s median output rate is 276.619 tokens per second, versus 128.056 for o3. In a streaming code assistant, that difference can shorten the visible wait after generation starts and make iterative interaction feel more fluid. It does not prove that Gemini produces better code, because the supplied benchmark material contains no coding score or verified community test.

Equal latency changes the interpretation. Both models show 0.3 seconds, so Gemini’s advantage is primarily generation throughput rather than faster request initiation. Short answers may therefore feel closer than the speed chart suggests. Long explanations, code patches, and multi-step outputs are more likely to expose the difference.

The general-intelligence result favors Gemini by 15 points, with scores of 45.4 and 30.4. That supports Gemini as the safer broad-task default, but it does not establish superiority in every domain. o3’s Math Index is 88.3, and the snapshot has no Gemini value for the same measure. A team building symbolic reasoning, quantitative planning, or mathematical verification should treat the missing Gemini result as unresolved evidence, not as a zero.

The research brief also found no reliable Reddit, Hacker News, or X posts with verifiable methods for either model’s coding experience, speed perception, or behavioral tendencies. Developers should run representative prompts before making a high-stakes migration. Data provided by https://artificialanalysis.ai/

Gemini 3.5 Flash (medium) is marginally cheaper on blended usage, but o3 becomes cheaper when generated output dominates the bill.

The listed blended prices are close: $3.375 per 1M tokens for Gemini and $3.5 for o3. That small difference favors Gemini for workloads with a balanced input and output mix matching the supplied 3-to-1 assumption. It is not large enough to justify choosing Gemini when a mathematical failure would create review, retry, or correction costs.

Input and output change the decision. Gemini’s input price is $1.5 per 1M tokens, compared with $2 for o3. o3’s output price is $8 per 1M tokens, compared with $9 for Gemini. Retrieval-heavy systems that send large prompts repeatedly may benefit from Gemini’s cheaper input. Systems that generate long reports, code changes, or reasoning traces may find o3’s cheaper output more attractive.

The cost chart also excludes several operational questions. Google’s pricing page states that paid Gemini tiers support context caching, and that Google Search and Google Maps grounding each have a shared monthly free allowance of 5,000 queries before excess queries are charged at $14 per 1,000 queries. Google’s pricing documentation also states that the free tier does not provide Search or Maps grounding. Grounding can therefore change the effective cost of a retrieval-enabled application.

OpenAI’s supplied pricing page does not list o3’s current Standard, Batch, Flex, or Fast mode prices. OpenAI’s pricing documentation therefore cannot validate the snapshot price as a currently listed public tariff. Treat the o3 price as comparison data requiring a live account check before procurement.

Gemini 3.5 Flash (medium) is the recommended starting point for general-purpose applications, provided the exact Google endpoint is verified before release.

Choose Gemini for customer-facing assistants, coding workflows, document transformation, and other applications where broad capability and fast streaming matter. The supplied Intelligence Index favors Gemini at 45.4 versus 30.4, and its median output speed is 276.619 tokens per second. The lower input price of $1.5 per 1M tokens also suits applications that repeatedly send substantial context. Data provided by https://artificialanalysis.ai/

Choose o3 when mathematical reasoning is a hard requirement and the application can tolerate slower generation. o3 is the only model with a supplied Math Index, at 88.3. That does not prove that o3 wins every quantitative task, but it is the strongest direct evidence for the specialized use case in this comparison. The absence of a Gemini Math Index makes a clean head-to-head conclusion impossible.

Use a two-model evaluation when the application combines both profiles. Route broad conversational or high-volume generation to Gemini, then test o3 on the narrow mathematical or verification stage. This architecture adds routing complexity, so it is justified only when mathematical accuracy has a measurable business impact.

Before committing, verify three facts in the target accounts. First, confirm that the internal label Gemini 3.5 Flash (medium) maps to Google’s documented gemini-3.5-flash. Second, confirm that o3 is callable and priced under the intended OpenAI account. Third, test context limits, structured output behavior, tool calling, and failure recovery, because the supplied official pages do not provide those details for either comparison label. Google’s model documentation and OpenAI’s model directory leave those production questions open.

Gemini 3.5 Flash (medium) has better documented current visibility than o3, but neither comparison label has a complete public capability record in the supplied sources.

Google’s official pages provide a Stable gemini-3.5-flash entry, pricing, and grounding rules. They do not provide a separate official entry for the medium variant, its context window, maximum output, API parameters, multimodal range, or official benchmark results. Google’s model documentation is therefore useful for endpoint verification, not for filling every capability gap.

OpenAI’s supplied current model directory and pricing page do not list o3. They also do not establish a stable alias, direct availability, current replacement status, context window, output limit, API parameters, or multimodal scope. OpenAI’s pricing documentation cannot confirm the o3 price as a current public listing.

The release dates in the data snapshot are 2026-05-19 for Gemini 3.5 Flash (medium) and 2025-04-16 for o3, but the research brief did not find a matching official Google announcement for the Gemini date. These dates should not be treated as independently verified launch announcements.

Community evidence is also insufficient. The research brief found no reliable, methodologically documented Reddit, Hacker News, or X posts for either model. Claims about coding feel, refusal patterns, hidden limits, or real-world reliability remain unproven here.