Gemini 3 Deep Think vs Grok-1: The Ultimate Performance & Pricing Comparison

Gemini 3 Deep Think and Grok-1 are not currently comparable production choices because neither model has a clearly verified current API path. Google’s official model documentation does not list Gemini 3 Deep Think, its API alias, context window, output limit, parameters, multimodal capabilities, or official benchmark results (Gemini API model documentation). xAI’s current model directory does not list Grok-1 and instead directs general coding and other tasks toward Grok 4.6 (xAI Models).

The practical conclusion is simple: developers should treat this comparison as an evidence and availability review, not as a normal capability shootout. The data snapshot reports 0 for blended pricing, input pricing, output pricing, median output tokens per second, and latency for both models. Those zeros do not prove free access or instant responses. They indicate that the dataset has no usable current value for those fields.

Data provided by https://artificialanalysis.ai/ should therefore be read alongside the documentation gap. A model that cannot be mapped to a stable endpoint cannot be selected responsibly for a new production integration, even if its name suggests advanced reasoning or a historical product identity.

Grok-1 has the only reported intelligence score, but Gemini 3 Deep Think has no verified public model identity in the cited Google API documentation.

Grok-1 therefore has a narrow evidence advantage, not a proven capability advantage. The score of 5.8 is useful only as a recorded measurement for Grok-1. It cannot show that Grok-1 is better than Gemini 3 Deep Think because Gemini has no matching score in the supplied data.

Gemini 3 Deep Think has a stronger conceptual fit only if the team already has a confirmed Google access route from another source. The cited Google documentation does not provide that confirmation. Grok-1 faces the same operational problem, with xAI’s current documentation pointing developers toward a newer model family instead.

The selection answer is thus a risk answer: do not commit either name to a new integration until endpoint availability, pricing, limits, and replacement guidance are verified directly with the vendor.

Gemini 3 Deep Think has no verified performance record in the supplied evidence, while Grok-1 has only a single reported intelligence index of 5.8.

The benchmark chart below the article may show many empty comparison fields, and that absence is more important than any apparent tie. Gemini 3 Deep Think has no reported value for the Artificial Analysis intelligence index, coding index, math index, MMLU Pro, GPQA, HLE, LiveCodeBench, SciCode, Math 500, AIME, AIME Twenty-Five, IFBench, LCR, TerminalBench Hard, TerminalBench version two point one, Tau Two, or Tau Banking. Grok-1 has no reported value for those measures either, except for the intelligence index value of 5.8.

The performance fields also report 0 median output tokens per second and 0 latency seconds for both models. These values cannot support a speed ranking. They may represent unavailable measurements, not observed runtime behavior. A developer deciding between interactive coding assistance and background batch work therefore has no supplied evidence about response speed, time to first token, long-context stability, tool use, or sustained output quality.

The missing comparison changes the testing requirement. Teams should build a small task set that reflects their product, then verify the exact endpoint behind each model name. Coding tasks should include repository edits, debugging, structured output, and tool calls. Reasoning tasks should include the failure cases that matter to the product. The current sources do not establish any of those behaviors for either model. Community evidence cannot fill the gap because the brief found no reliable Reddit, Hacker News, or X posts that could confirm coding experience, speed, model preferences, or failure patterns for either model.

A performance winner cannot be named from this record. Grok-1’s 5.8 is a data point, not a head-to-head result.

Gemini 3 Deep Think and Grok-1 tie at $0 in the supplied price fields, but neither model has a verified current official price.

The cost chart below the article should be treated as a data-availability view. The snapshot records $0 for each model’s blended price, input-token price, and output-token price. It also records a tie in every pricing comparison. That does not mean a developer can send production traffic to either model for free. The Google pricing documentation does not list independent pricing for Gemini 3 Deep Think, including input, output, batch, cached, or priority pricing (Google Gemini API pricing). The xAI model documentation does not list a current Grok-1 price (xAI Models).

The commercial risk is larger than the missing dollar amount. If a model name is unavailable, a team may spend engineering time building an adapter that cannot be deployed. If the name points to a legacy or replaced model, migration work can become the real cost. A supposedly cheaper model may also become more expensive if it requires extra retries, manual review, prompt workarounds, or a second model for tasks it cannot reliably complete. The supplied research does not measure any of those factors for either model.

Before approving a budget, verify the exact vendor endpoint, billing unit, input and output rates, minimum commitments, rate limits, and any premium routing option. The current evidence supports only one cost conclusion: no trustworthy price comparison is available. The $0 entries should not be used in a business case, forecast, or procurement decision.

Gemini 3 Deep Think and Grok-1 should both remain unapproved for a new production integration until their current access paths are verified.

For a team already using Google infrastructure, Gemini 3 Deep Think may be worth investigating as a candidate. The correct next step is to confirm whether Google exposes that exact name through a current API, whether it is an internal or temporary label, and which documented Gemini model should replace it. Google’s official model page does not currently provide those answers (Gemini API model documentation).

For a team already using xAI infrastructure, Grok-1 may be relevant for historical compatibility or an existing experiment. It is not a safe default for new work because xAI’s current documentation presents Grok 4.6 for general coding and other tasks, while leaving Grok-1 absent from the current model directory (xAI Models). The documentation also does not provide a confirmed Grok-1 replacement statement, shutdown statement, stable alias, or current price.

The decision rule should be operational:

• Select a model only after the vendor confirms the callable endpoint and stable alias.
• Require a current price and limit sheet before estimating unit economics.
• Run representative tasks before comparing coding quality, reasoning quality, latency, or tool behavior.

If a team must start work immediately, it should choose a currently documented model from the relevant vendor directory and record the selected model identifier in configuration. Neither supplied source set proves that Gemini 3 Deep Think or Grok-1 is suitable for a fresh production commitment.

Data provided by https://artificialanalysis.ai/ supplies the comparison snapshot, but the snapshot cannot replace current vendor documentation. The strongest recommendation is to resolve identity and availability first, then compare capability and cost.

Gemini 3 Deep Think and Grok-1 both require an availability check before engineering work begins. The questions below focus on the risks that benchmark tables cannot answer.