Model comparison
Gemini 3.6 Flash vs Gemini 3.7 Flash
Two Google tiers compared on the figures that decide which one a workload actually needs.
Catalog record checked August 14, 2026; individual provider fields may change.
Gemini 3.6 Flash
Budget
Gemini 3.7 Flash
Budget
| Specification | Gemini 3.6 Flash | Gemini 3.7 Flash |
|---|---|---|
| Provider | ||
| Tier | Budget | Budget |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 66K |
| Input / 1M tokens | $0.75 | $0.75 |
| Output / 1M tokens | $3.75 | $3.75 |
| Weights | Closed | Closed |
| Parameters | Not disclosed | Not disclosed |
| Reasoning levels | minimal, low, medium, high | low, medium, high |
| Modalities | text, image, video, audio, pdf | text, image, video, audio, pdf |
| Released | July 21, 2026 | August 13, 2026 |
| Artificial Analysis Intelligence Index [high] (2026-08-08) | 52 | Winner: 56 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: Gemini 3.6 Flash: Introductory paid-tier rates through 2026-12-31. From 2027-01-01 the standard paid rates are $1.50 / $7.50 per million tokens (Gemini API pricing). Cache is $0.075 / MTok through 2026-12-31, then $0.15. Wire calls to `gemini-3.6-flash`. Thinking levels are `minimal` / `low` / `medium` / `high` (API default medium). · Gemini 3.7 Flash: Introductory paid-tier rates through 2026-12-31. From 2027-01-01 the standard paid rates are $1.50 / $7.50 per million tokens (Gemini API pricing). Cache is $0.075 / MTok through 2026-12-31, then $0.15. Wire calls to `gemini-3.7-flash`. Thinking levels are `low` / `medium` / `high` (API default medium; no MINIMAL).
Gemini 3.6 Flash
Google's July 2026 Flash tier for multimodal volume work; 3.7 Flash is the newer coding/agent workhorse at the same introductory rate.
Best for
- Multimodal pipelines
- High-volume processing
- Video and audio input
Watch out
Introductory $0.75 input is 3.75× Luna; the $1.50 rate returns on 2027-01-01. 66K max output (65,536 tokens) is roughly half Luna's. Prefer 3.7 Flash for new coding-agent work unless you are pinned to 3.6.
Gemini 3.7 Flash
Google's 2026-08-13 Flash workhorse for coding and agents, three weeks after 3.6 Flash, at an introductory $0.75 / $3.75 per million tokens.
Best for
- Coding agents
- Multimodal agent loops
- High-volume Google API work
Watch out
Intro price doubles on 2027-01-01. DeepSWE 1.1 best-effort (high) is 65.3% at $2.18/task; medium scores 65.5% a few cents cheaper. 66K max output (65,536 tokens). Knowledge cutoff is March 2026 on some domains (January 2025 on others).
Benchmark
DeepSWE 1.1 in context
Both models shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
Gemini 3.6 Flash vs Gemini 3.7 Flash
Answered from the verified figures on this page rather than general guidance.
Is Gemini 3.6 Flash or Gemini 3.7 Flash cheaper for input?
Both cost $0.75 per million input tokens at standard rates, so input price is not a deciding factor between them.
Is Gemini 3.6 Flash or Gemini 3.7 Flash cheaper for output?
Both cost $3.75 per million output tokens at standard rates, so output price is not a deciding factor between them.
Which has the larger context window, Gemini 3.6 Flash or Gemini 3.7 Flash?
Both accept about 1.05M tokens of context, so document length will not decide between them.
Do Gemini 3.6 Flash and Gemini 3.7 Flash support the same reasoning levels?
Gemini 3.6 Flash exposes minimal, low, medium, high, while Gemini 3.7 Flash exposes low, medium, high.
Should I use Gemini 3.6 Flash or Gemini 3.7 Flash?
Both sit in the budget tier, so the choice usually comes down to price and context rather than capability. Gemini 3.6 Flash suits multimodal pipelines; Gemini 3.7 Flash suits coding agents.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.