Gemini 3.5 FlashvsOpus 4.7
Across 3 shared benchmarks, Gemini 3.5 Flash leads overall: Gemini 3.5 Flash wins 3, Opus 4.7 wins 0, with 0 ties and an average score difference of +10.96.
Gemini 3.5 Flash
Google Deep Mind · 2026-06-20 · Multimodal model
Opus 4.7
Anthropic · 2026-04-16 · Reasoning model
Gemini 3.5 Flash3 wins(100%)(0%)0 winsOpus 4.7
Benchmark scores
Grouped by capability, sorted by largest gap within each. 3 shared benchmarks.
AI Agent - Tool Usage
Gemini 3.5 Flash 1/1| Benchmark | Gemini 3.5 Flash | Opus 4.7 | Diff |
|---|---|---|---|
| Terminal-Bench 2.1 | 76.2031 / 53Thinking High (With Tools) | 69.7040 / 53Thinking High (With Tools) | +6.50 |
Commonsense Reasoning
Gemini 3.5 Flash 1/1| Benchmark | Gemini 3.5 Flash | Opus 4.7 | Diff |
|---|---|---|---|
| SimpleBench | 76.7010 / 94Normal (No Tools) | 61.7027 / 94Normal (No Tools) | +15 |
Text Embedding
Gemini 3.5 Flash 1/1| Benchmark | Gemini 3.5 Flash | Opus 4.7 | Diff |
|---|---|---|---|
| Context Arena | 33.49109 / 126Normal (No Tools) | 22.12122 / 126Normal (No Tools) | +11.37 |
Specs
| Field | Gemini 3.5 Flash | Opus 4.7 |
|---|---|---|
| Publisher | Google Deep Mind | Anthropic |
| Release date | 2026-06-20 | 2026-04-16 |
| Model type | Multimodal model | Reasoning model |
| Architecture | Dense | Dense |
| Parameters | Not available | Not available |
| Context length | 1M | 1000K |
| Max output | 64K | 128K |
API pricing
Prices use DataLearner records when available; missing fields are not inferred.
| Item | Gemini 3.5 Flash | Opus 4.7 |
|---|---|---|
| Text input | $1.5 / 1M tokens | $5 / 1M tokens |
| Text output | $9 / 1M tokens | $25 / 1M tokens |
| Cache read | $0.15 / 1M tokens | $0.5 / 1M tokens |
| Cache write | Not public | $6.25 / 1M tokens |
Summary
- Gemini 3.5 Flashleads in:AI Agent - Tool Usage (1/1), Commonsense Reasoning (1/1), Text Embedding (1/1)
On average across the 3 shared benchmarks, Gemini 3.5 Flash scores 10.96 higher.
Largest single-benchmark gap: SimpleBench — Gemini 3.5 Flash 76.70 vs Opus 4.7 61.70 (+15).
Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.