Model comparison
Gemini 3.5 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 18, 2026; individual provider fields may change.
Gemini 3.5 Flash
Budget
Gemini 3.7 Flash
Budget
| Specification | Gemini 3.5 Flash | Gemini 3.7 Flash |
|---|---|---|
| Provider | ||
| Tier | Budget | Budget |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 66K |
| Input / 1M tokens | $1.50 | Winner: $0.75 |
| Output / 1M tokens | $9 | Winner: $3.75 |
| Weights | Closed | Closed |
| Parameters | Not disclosed | Not disclosed |
| Reasoning levels | Not verified | low, medium, high |
| Modalities | text, image, video, audio, pdf | text, image, video, audio, pdf |
| Released | May 19, 2026 | August 13, 2026 |
| Artificial Analysis Intelligence Index [high] (2026-08-14) | Not verified | 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 3.5 Flash: Google Gemini API standard paid tier is $1.50 input / $9 output per million tokens (agent verified 2026-08-18, https://ai.google.dev/gemini-api/docs/pricing). GA/stable API id `gemini-3.5-flash` (powers `gemini-flash-latest`). Token limits and modalities are from Google’s model card. · 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 3.5 Flash
Google’s generally available Gemini 3.5 Flash tier: 1M context, thinking-enabled, and the stable alias behind `gemini-flash-latest`. Distinct from Flash-Lite (cheaper throughput) and from 3.6/3.7 Flash (newer workhorses).
Best for
- Stable Google Flash alias
- Agent loops that must pin a GA id
- Multimodal volume when 3.7 is not required
Watch out
Do not treat this as Flash-Lite or as 3.7 Flash. Standard paid list is $1.50/$9; DeepSWE 1.1 high-effort is 36.1% at $3.45/task. Model card latest update: May 2026.
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.
When the cheaper one wins
Gemini 3.7 Flash is cheaper on output at $3.75 per million tokens against $9 for Gemini 3.5 Flash — about 2.4×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. On DeepSWE 1.1, Gemini 3.7 Flash is 65% Pass@1 at $2.18/task versus Gemini 3.5 Flash at 36.1% / $3.45/task. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerCommon questions
Gemini 3.5 Flash vs Gemini 3.7 Flash
Answered from the verified figures on this page rather than general guidance.
Is Gemini 3.5 Flash or Gemini 3.7 Flash cheaper for input?
Gemini 3.7 Flash is cheaper at $0.75 per million input tokens, against $1.50 for Gemini 3.5 Flash — roughly 2.0× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.5 Flash has tiered pricing: Google Gemini API standard paid tier is $1.50 input / $9 output per million tokens (agent verified 2026-08-18, https://ai.google.dev/gemini-api/docs/pricing). GA/stable API id `gemini-3.5-flash` (powers `gemini-flash-latest`). Token limits and modalities are from Google’s model card. Gemini 3.7 Flash has tiered pricing: 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).
Is Gemini 3.5 Flash or Gemini 3.7 Flash cheaper for output?
Gemini 3.7 Flash is cheaper at $3.75 per million output tokens, against $9 for Gemini 3.5 Flash — roughly 2.4× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.5 Flash has tiered pricing: Google Gemini API standard paid tier is $1.50 input / $9 output per million tokens (agent verified 2026-08-18, https://ai.google.dev/gemini-api/docs/pricing). GA/stable API id `gemini-3.5-flash` (powers `gemini-flash-latest`). Token limits and modalities are from Google’s model card. Gemini 3.7 Flash has tiered pricing: 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).
Which has the larger context window, Gemini 3.5 Flash or Gemini 3.7 Flash?
Both accept about 1.05M tokens of context, so document length will not decide between them.
Should I use Gemini 3.5 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 3.5 Flash suits stable google flash alias; 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.