Gemini 1.5 Pro (Sep '24) vs Gemini 3 Deep Think: The Ultimate Performance & Pricing Comparison

Gemini 1.5 Pro (Sep '24) is the only model in this comparison with recorded benchmark results, while Gemini 3 Deep Think cannot be verified as a public Google API model. The practical choice is therefore not a normal capability contest. It is a choice between measurable historical evidence and an unverified model identity.

The supplied data snapshot reports Gemini 1.5 Pro (Sep '24) with a 23.6 Artificial Analysis Coding Index, a 9.9 Artificial Analysis Intelligence Index, and a 0.75 MMLU Pro score. Gemini 3 Deep Think has no reported value on those evaluations. The same snapshot reports 0 for blended, input, and output pricing for both models, plus 0 for output speed and latency. Those zeros should be treated as missing or unconfirmed commercial data, not as proof of free access or equal responsiveness.

Google's current Gemini API model documentation does not list Gemini 3 Deep Think and does not retain an active model card for Gemini 1.5 Pro (Sep '24). The supplied benchmark attribution is: Data provided by https://artificialanalysis.ai/

Gemini 1.5 Pro (Sep '24) is the more defensible selection because developers can at least inspect its recorded results, while Gemini 3 Deep Think has no verified public API identity in the supplied evidence.

The comparison produces an unusual result:

The official Gemini API pricing page does not list current pricing for either named model. That creates a key distinction between data availability and product availability. Gemini 1.5 Pro has historical measurements, but the current product surface does not clearly support selecting that exact version. Gemini 3 Deep Think has neither current product confirmation nor comparable measurements.

For a production team, this means the article cannot honestly declare a capability winner between the two models. It can identify the safer evidence-based hypothesis: start with Gemini 1.5 Pro's measured profile only if Google or your provider confirms a working replacement endpoint. Otherwise, pause the comparison and request a model identifier that appears in an active catalog.

Gemini 1.5 Pro (Sep '24) offers the stronger evidence base for coding and general reasoning, but the available scores cannot prove that its current API behavior is deployable.

The supplied measurements show Gemini 1.5 Pro (Sep '24) at 23.6 on the Artificial Analysis Coding Index and 9.9 on the Artificial Analysis Intelligence Index. It also records 0.316 on LiveCodeBench, 0.295 on SciCode, 0.876 on Math 500, 0.23 on AIME, 0.589 on GPQA, 0.046 on HLE, and 0.75 on MMLU Pro. Gemini 3 Deep Think has no value reported for any of these evaluations. That does not mean Gemini 3 Deep Think performs poorly. It means the supplied evidence cannot rank it.

For developers, the most useful implication is confidence, not a raw leaderboard position. Gemini 1.5 Pro has enough recorded signal to justify a test plan around code generation, mathematical reasoning, scientific code, and difficult question answering. A team can turn those areas into acceptance tests and compare actual outputs against the historical profile. Gemini 3 Deep Think cannot support the same process because there is no verified model entry, official parameter page, or reproducible community evaluation in the research brief.

The performance story also has an important boundary. Google's Gemini API model documentation describes Gemini 1.5 Pro as a multimodal model for complex tasks, with text, image, video, and audio input and text output. The same official documentation no longer preserves a current parameter table for the specific Sep '24 version. The historical long-context positioning is therefore useful as product context, but the exact context window and output limit for this version are not confirmed by the supplied current page.

The reported speed comparison is a tie at 0 median output tokens per second, and the latency comparison is also a tie at 0 seconds. These values do not show that the models respond equally fast. They show that the snapshot contains no usable speed or latency measurement for either model. A developer building an interactive coding assistant should measure time to first token, full response time, timeout behavior, and retry frequency on the actual endpoint.

The evidence gap can change the decision. If Gemini 3 Deep Think later receives a verified endpoint and independent testing shows better reliability on long code reviews or difficult reasoning tasks, it could become the better technical choice. The current brief provides no such evidence, so selecting it today would be a bet on an unverified product name rather than a benchmark-backed decision.

Gemini 1.5 Pro (Sep '24) and Gemini 3 Deep Think are tied at $0 in the supplied snapshot, but neither model has a confirmed current Google price.

The data brief reports $0 per 1M blended tokens, $0 per 1M input tokens, and $0 per 1M output tokens for both models. Those values are not enough to conclude that either model is free. The research brief separately says the current Gemini API pricing documentation does not list Gemini 1.5 Pro (Sep '24) or Gemini 3 Deep Think. The most responsible reading is that the comparison lacks confirmed price data.

This matters because an unavailable model can be more expensive than a costly model that works. Engineering time spent replacing an invalid endpoint, rewriting prompts, validating multimodal behavior, and investigating undocumented limits can exceed the token bill. A model with a visible price may also be cheaper in practice if it reduces retries, manual review, or fallback traffic. None of those operational costs can be calculated from the supplied data, so the article does not assign a total-cost winner.

The cost conclusion may also reverse after endpoint verification. If a provider confirms that Gemini 1.5 Pro can be called through a supported alias, its historical benchmark record gives the team a basis for measuring quality per dollar. If Gemini 3 Deep Think receives a valid price and materially reduces failed generations, its effective cost could be lower even if its listed token price is higher. The current evidence cannot test either scenario.

Before procurement or production rollout, the team should obtain three facts in writing: the exact callable model identifier, the input and output token prices, and the billing treatment for retries or batch requests. The supplied snapshot establishes none of those facts for either model.

Gemini 1.5 Pro (Sep '24) is the better starting hypothesis, but developers should not ship either name until an active endpoint and current price are verified.

Choose Gemini 1.5 Pro (Sep '24) for an evaluation track when your team values measurable historical evidence. Its recorded 23.6 coding index and 0.75 MMLU Pro result provide concrete starting points for a test suite. The result is still conditional because Google's current Gemini API model documentation no longer shows this exact version as an active model entry.

Do not choose Gemini 3 Deep Think based on its name or release metadata alone. The data snapshot gives it a release date of 2026-02-05, but the research brief found no matching official model name, API alias, context window, output limit, multimodal description, benchmark result, pricing entry, or reproducible community test. A newer label is not evidence of better coding quality or production readiness.

The selection rule for a real project is simple:

1. Verify the exact model identifier in the provider's active catalog.
2. Run the same developer tasks against the verified endpoint, including repository-level code changes, long-context review, structured output, and failure recovery.
3. Compare quality, latency, retry rate, and actual billing before committing to an architecture.

If only the two names in this brief are available, select neither for production. Use Gemini 1.5 Pro as the research baseline because it has measured evidence, and keep Gemini 3 Deep Think unranked until its identity and access path are confirmed. This recommendation reflects deployment confidence, not a claim that Gemini 1.5 Pro is currently available or technically superior in every task.

Gemini 1.5 Pro (Sep '24) has more documented historical evidence, but the supplied materials cannot answer several questions that normally decide a production model choice.

The research brief contains no reproducible community reports for either model's coding experience, response feel, stability, or recurring failure patterns. It also contains no current endpoint confirmation, exact version parameters, verified pricing, or side-by-side benchmark result. Google's Gemini API model documentation and Gemini API pricing documentation are the relevant official checks, but they do not close every historical-version gap described here.

The article therefore separates three claims: Gemini 1.5 Pro has recorded measurements, Gemini 3 Deep Think has no supplied measurements, and neither model has confirmed current commercial details. That distinction should guide the next evaluation step.