Google's Gemini 3.6 Flash Scores 49% on Coding Benchmark While Cutting Costs 52%
Gemini 3.6 Flash hits 49% on DeepSWE — matching Claude Opus 4.8 at medium reasoning — while using 65% fewer tokens and costing 52% less per task.
- 49% on DeepSWE: Gemini 3.6 Flash scores 49% (up from 37%), matching Claude Opus 4.8 at medium reasoning effort.
- 65% fewer tokens: On long-horizon coding tasks, 3.6 Flash uses up to 65% fewer output tokens than its predecessor, cutting cost per task by 52%.
- New pricing: $1.50/1M input tokens and $7.50/1M output tokens — down from $9.00/1M output on 3.5 Flash.
- Broader gains: MLE-Bench jumps from 49.7% to 63.9%; OSWorld-Verified (computer use) hits 83.0%, best in class.
- 1M context, updated knowledge: Both 3.6 Flash and 3.5 Flash-Lite share a 1M-token context window and a March 2026 knowledge cutoff.
- Available now: Accessible via the Gemini API and Google AI Studio; full leaderboard at DeepSWE.
Google just released Gemini 3.6 Flash, and the headline number is deceptively simple: 49% on DeepSWE, up from 37% for its predecessor. The real story is that the model achieved that score while spending dramatically fewer tokens to get there. That combination is rare, and it changes the math for anyone running coding agents at scale.
What landed on the leaderboard
Google launched three new Gemini models aimed at making AI agents faster, cheaper, and more efficient: Gemini 3.6 Flash, Gemini 3.5 Flash Lite, and Gemini 3.5 Flash Cyber. Gemini 3.6 Flash is the flagship of the three and the one that matters most for coding agent workloads.
The model scored 49% on DeepSWE, compared with 37% for Gemini 3.5 Flash, a 12-point absolute improvement on a benchmark specifically designed to be hard to game. Higher precision and fewer unnecessary code changes drove the gain.
What DeepSWE actually measures
Most public coding benchmarks are saturating. Top models cluster within a narrow score band where differences fall inside the margin of error. DeepSWE was built to fix that:
- Contamination-free: Tasks are written from scratch, not adapted from existing commits or PRs, so no model has seen the solution during pretraining.
- High diversity: 113 tasks across 91 repositories in 5 languages: TypeScript, Go, Python, JavaScript, and Rust.
- Real-world complexity: