Gemini 3.5 Flash: Google's Official Follow-up Release Comparison of Efficiency and Capabilities

One-sentence takeaway

Google's follow-up release uses Gemini 3.5 Flash as the baseline for 3.6 Flash: 3.6 outperforms 3.5 on DeepSWE, MLE Bench, OSWorld-Verified, and GDPval-AA v2, but these are official comparisons and cannot replace independent retesting of 3.5 on its own task set.

Use cases

• Suitable tasks: Understanding 3.5 Flash's baseline position in Google's subsequent versions, especially for version comparisons involving coding, computer use, knowledge work, and Agent cost efficiency.

• Unsuitable tasks: Treating the difference between 3.6 and 3.5 as an absolute cross-model ranking, or overlooking thinking levels, tools, and the harness.

• Applicable model version: Gemini 3.5 Flash (as the official baseline for 3.6 Flash).

• Applicable client, Agent, or API: Google Gemini API and enterprise Agent scenarios; the article says 3.6's computer use can serve as a built-in client tool in the API, while 3.5's specific integration limitations require checking the model page separately.

• Recommended reasoning levels and parameters: The original article does not disclose the complete thinking level, sampling parameters, or number of runs for each comparison; parameters that cannot be verified should not be added.

Test environment

• Comparison: Gemini 3.6 Flash vs Gemini 3.5 Flash.

• Sources: Google's official release materials, citing the Artificial Analysis Index, Datacurve DeepSWE, MLE Bench, OSWorld-Verified, and GDPval-AA v2.

• Environment details: The article does not disclose all test inputs, sample sizes, random seeds, tool harnesses, or confidence intervals.

Input/configuration

• The article describes 3.6 Flash as an iteration based on feedback from 3.5 Flash, emphasizing fewer output tokens, fewer reasoning steps, and fewer tool calls.

• 3.6 Flash is listed at $1.50 per million input tokens and $7.50 per million output tokens; these are 3.6 prices and should not be misremembered as 3.5's current prices.

• The 3.5 Flash model page separately lists 1,048,576 input tokens, 65,536 output tokens, and support for thinking, function calling, code execution, Search grounding, and preview computer use.

Results data

Conclusion

The official comparison supports the upgrade judgment that “3.6 is more efficient and delivers better quality than 3.5,” especially for coding, ML research, computer use, and knowledge work. However, it only tells us 3.5's relative baseline and is insufficient to determine whether 3.5 is suitable for a specific production task.

Limitations

• All figures come from Google's official release; third-party links are cited, but the page does not provide complete original experimental records.

• The inputs, tools, reasoning levels, number of runs, and scoring implementations for the different benchmarks are not fully disclosed in the article.

• The prices, model IDs, context, and tool availability of 3.5 and 3.6 may change over time or by plan; use the current model page for the target API as the reference.

• 3.6's advantages cannot be used to infer 3.5's absolute ranking against models from other vendors.

Reproduction steps

1. Fix the gemini-3.5-flash model version, thinking level, context, tools, and output limit.

2. Run DeepSWE, MLE Bench, and OSWorld-like tasks in the same code repository/computer environment, repeating them at least several times and recording success rate, tokens, tool calls, and total time.

3. Retest with the same harness used for 3.6 to avoid conflating a version upgrade with a test-framework upgrade.

4. Use the official difference as a reference range, and separately report your own confidence intervals, failure types, and costs.

Source excerpt or observation (for compliant short quotation only)

Google describes 3.5 Flash as the direct feedback baseline for 3.6 Flash and says that 3.6 “takes fewer reasoning steps and tool calls.” This can guide version migration evaluations, but it is not an end-to-end acceptance test of 3.5 on its own.