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Gemini 4 Carbon vs Gemini 4 Argon: benchmarks and token cost

Compare Gemini 4 Carbon (High) vs Gemini 4 Argon (High) with benchmark results, speed, pricing, and practical workflow guidance.

Best For Gemini 4 Carbon (High)

  • Evaluate this exact variant against a fixed set of representative tasks.

Best For Gemini 4 Argon (High)

  • Compare accepted-result cost and latency under the same task requirements.

Two Google families with equal captured rates. Compare the captured model variants, overall index, token pricing and response metrics.

This comparison uses Gemini 4 Carbon (High) and Gemini 4 Argon (High). It reports the captured benchmark data, with no new hands-on test.

Two Google families with equal captured rates

Carbon scores 59.4 on the captured overall index, while Argon scores 50.4. Both have listed input and output rates of $4 and $20 per million tokens. The shared provider and rate make this useful for an evaluation of model quality and latency under comparable accounting, while keeping the High variants and their specific measured settings explicit.

How to read the table

The intelligence index is an aggregate measurement, not a percentage of correct answers in your application. Input and output prices are separate rates per million tokens. Coding and math indices are unavailable in this capture and must not be read as zero. Compare first-token latency separately from output-token throughput, and use the same model effort settings for a trial. Choose using task quality, total time and the cost of accepted results; provider features and deployment requirements need their own checks.

Benchmark table

Scores, speed, and pricing captured for this analysis. Open a model page for the latest results.

Metric Google Gemini 4 Carbon (High) Google Gemini 4 Argon (High)
Index Scores
Intelligence Index 59.4 50.4
Coding Index--
Math Index--
Benchmark Scores

Verdict

Carbon scores 59.4 on the captured overall index, while Argon scores 50.4. Both have listed input and output rates of $4 and $20 per million tokens. The shared provider and rate make this useful for an evaluation of model quality and latency under comparable accounting, while keeping the High variants and their specific measured settings explicit.

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