Gemini 3 Deep Think vs GPT-4o (Nov '24): Which Model Should Developers Choose?

Gemini 3 Deep Think is the cheaper listed option, but GPT-4o (Nov '24) is the safer developer choice because it is the only model with reported evaluation results. The supplied data shows Gemini 3 Deep Think at $0 per 1M blended tokens, while GPT-4o (Nov '24) is listed at $4.375. That apparent price advantage does not prove free production access. Google’s current Gemini API model documentation does not list Gemini 3 Deep Think by name, and Google’s Gemini API pricing documentation does not provide a dedicated price for it.\n\nGPT-4o (Nov '24) also has an incomplete current status. OpenAI’s current model directory does not list gpt-4o, and OpenAI’s current pricing directory does not list its price. The data snapshot nevertheless reports GPT-4o (Nov '24) at $2.5 per 1M input tokens and $10 per 1M output tokens. Developers should treat those values as snapshot data, not as proof of current account-level availability.\n\nThe central comparison is therefore not simply capability versus cost. It is a choice between one model with measurable historical evidence and another with a nominally lower price but no confirmed operating contract in the supplied sources.

GPT-4o (Nov '24) has the stronger selection case because the supplied snapshot gives it measurable results, while Gemini 3 Deep Think has no reported evaluation values. Artificial Analysis reports GPT-4o (Nov '24) with an Artificial Analysis Intelligence Index of 11.1, an Artificial Analysis Math Index of 6, an MMLU-Pro score of 0.748, and a GPQA score of 0.543. The same snapshot reports no values for Gemini 3 Deep Think across the listed evaluations.\n\nThat does not establish that GPT-4o (Nov '24) is more capable than Gemini 3 Deep Think. It establishes that the supplied evidence can support a measured claim for GPT-4o, but not a measured claim for Gemini. Google’s model documentation does not identify Gemini 3 Deep Think as a current model or stable API alias. OpenAI’s model documentation does not identify gpt-4o in the current catalog either. Both labels therefore require a version and access check before production selection.\n\nFor a developer, the practical distinction is operational confidence. GPT-4o has a visible evaluation record in the snapshot, but its current listing status is unclear. Gemini 3 Deep Think has neither a visible evaluation record nor a confirmed official endpoint in the supplied research. The evidence gap is the most important result of this comparison.

GPT-4o (Nov '24) is the only model in this comparison with reported benchmark evidence, so GPT-4o is the provisional performance choice rather than a proven universal winner. Artificial Analysis reports GPT-4o (Nov '24) at 0.309 on LiveCodeBench, 0.333 on SciCode, 0.759333333333333 on Math-500, 0.15 on AIME, and 0.06 on AIME 25. The snapshot also reports 0 on LCR and 0.0833333333333333 on TerminalBench Hard. Gemini 3 Deep Think has null values for these evaluations.\n\nThe missing Gemini values change how the chart should be read. A blank result is not a low score, and it cannot justify saying GPT-4o wins the underlying task. The chart can show that GPT-4o has recorded observations and Gemini does not. It cannot show whether Gemini would outperform GPT-4o on coding, mathematics, reasoning, or long-context work.\n\nThe supplied research also found no reliable community posts that verify Gemini 3 Deep Think’s coding experience, response speed, or behavior preferences. It found no reliable version-specific community evidence for GPT-4o (Nov '24) either. Google’s Gemini API model documentation and OpenAI’s model documentation also do not provide the missing version-specific benchmark context.\n\nThe safe interpretation is narrow: GPT-4o is testable against the supplied results, while Gemini 3 Deep Think remains unproven. Teams should run representative repository tasks before treating the performance conclusion as final.

Gemini 3 Deep Think appears cheaper because the snapshot records $0 for every listed price field, but the evidence does not confirm that developers can call it for free. Artificial Analysis lists Gemini 3 Deep Think at $0 per 1M blended tokens, $0 per 1M input tokens, and $0 per 1M output tokens. GPT-4o (Nov '24) is listed at $4.375 per 1M blended tokens, $2.5 per 1M input tokens, and $10 per 1M output tokens.\n\nGoogle’s pricing documentation does not list a dedicated Gemini 3 Deep Think price. That makes the zero value ambiguous. It could represent a free offering, an unavailable model, an internal label, or an unpriced entry. The supplied research cannot distinguish those cases.\n\nThe economic decision can also reverse in practice. An unavailable endpoint creates engineering cost through migration, fallback routing, failed tests, and delayed delivery. A model with a lower token price can therefore become more expensive for a production team if access, limits, or version stability are not confirmed. GPT-4o’s listed blended price of $4.375 is higher, but the snapshot provides a concrete commercial reference for it.\n\nDevelopers should use the chart as a pricing signal, not a purchasing decision. Confirm endpoint access, billing behavior, quotas, and the exact model identifier in the target account before estimating operating cost.

GPT-4o (Nov '24) is the better default for a decision that must be made from the supplied evidence, while Gemini 3 Deep Think belongs in a validation queue. GPT-4o has reported results across general intelligence, mathematics, code-related evaluations, and agent-style tasks. Artificial Analysis reports an Artificial Analysis Intelligence Index of 11.1, a Math Index of 6, MMLU-Pro at 0.748, and GPQA at 0.543. Those values do not prove production quality, but they give a team something concrete to compare with its own acceptance tests.\n\nGemini 3 Deep Think should be selected only after Google confirms the exact callable model name. Google’s Gemini API model documentation does not list the name, and Google’s pricing documentation does not list a dedicated price. The supplied research also found no reliable community reports describing its failure modes or developer experience.\n\nGPT-4o (Nov '24) still needs a status check. OpenAI’s current model directory does not list gpt-4o, and OpenAI’s pricing directory does not list its current price.\n\nThe decision rule is straightforward. Use GPT-4o for a benchmark-backed prototype if the account still exposes the model. Test Gemini only as an experiment until its endpoint and price are verified. Reject either label for production if the exact version cannot be pinned and monitored.

GPT-4o (Nov '24) is the safer starting point because its supplied snapshot contains benchmark results, while Gemini 3 Deep Think has no reported evaluation values. Artificial Analysis reports GPT-4o (Nov '24) at 0.748 on MMLU-Pro and 0.543 on GPQA, but reports no corresponding Gemini values.\n\nGemini 3 Deep Think is not confirmed as a currently callable Google model in the supplied evidence. Google’s Gemini API model documentation does not list that model name or a stable alias.\n\nGPT-4o (Nov '24) is not confirmed as currently callable either. OpenAI’s current model directory does not list gpt-4o, so teams must verify access before implementation.\n\nThe displayed zero price for Gemini should not be treated as confirmed free usage. Google’s Gemini API pricing documentation does not provide dedicated pricing for Gemini 3 Deep Think.\n\nNeither model has a confirmed speed advantage in the supplied data. Artificial Analysis records 0 median output tokens per second and 0 latency seconds for both models, which supports a tie in the snapshot but does not provide a useful real-world latency estimate.