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Model comparison

Gemini 3.6 Flash vs Gemini 3.8 Flash

Two Google tiers compared on the figures that decide which one a workload actually needs.

Catalog record checked September 5, 2026Individual provider fields may changeEvidence confidence: High — see receipts below

Google

Gemini 3.6 Flash

Balanced

vs

Google

Gemini 3.8 Flash

Balanced

AI model capability comparison
SpecificationGemini 3.6 FlashGemini 3.8 Flash
ProviderGoogleGoogle
TierBalancedBalanced
Context window1MWinner: 1.05M
Max output64KWinner: 66K
Input / 1M tokens$0.75$0.75
Output / 1M tokens$3.75$3.75
WeightsClosedClosed
ParametersNot disclosedUnverifiedNot disclosedUnverified
Reasoning levelslow, medium, highlow, medium, high
Modalitiestext, image, video, audio, pdftext, image, video, audio, pdf
API model idgemini-3.6-flashgemini-3.8-flash
ReleasedJuly 21, 2026September 2, 2026
Artificial Analysis Intelligence Index (2026-08-14)54Not verifiedUnverified
Artificial Analysis Intelligence Index [high] (2026-09-02)Not verifiedUnverified58.7
Terminal-Bench 2.1 (2026-07)78Winner: 89.4
DeepSWE 1.1 (2026-07)48.6Not verifiedUnverified
Humanity's Last Exam (2026-08)51.2Winner: 54.9

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.

Benchmark receipts

  • 2026-09-02: Google official blog (vendor, HLE-Verified full set)Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise. Comparability: comparable with caveat — Subset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
  • 2026-09-02: Google launch eval table (transcribed by Vellum)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
  • 2026-09-02: Artificial AnalysisComposite index blending reasoning, knowledge, and coding evals into one 0–100 score. Comparability: directly comparable — AA occasionally rebaselines the index scale between snapshots — a score captured on one date is only comparable to same-snapshot scores (check measuredAt).
  • 2026-08: Google DeepMind model evaluation report (vendor, HLE-Verified full set)Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise. Comparability: comparable with caveat — Subset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
  • 2026-07: Google DeepMind model evaluation report (vendor, quoting Datacurve leaderboard; 3.7 report shows 49 — rounding conflict preserved)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.

Pricing tiers: Gemini 3.6 Flash: $0.75/$3.75 per MTok (intro through 2026-12-31, then $1.50/$7.50). Multimodal input. · Gemini 3.8 Flash: Intro $0.75/$3.75 per MTok through 2026-12-31, rising to $1.50/$7.50 from 2027-01-01; batch/Flex half price. Free tier available. Companion Gemini 3.8 Flash Cyber (defensive security) is restricted to the Fairwind Program with no public pricing.

BalancedRecord checked September 5, 2026

Gemini 3.6 Flash

Gemini 3.6 Flash is Google's balanced multimodal Flash tier with a 1M-token context.

Best for

  • Multimodal pipelines
  • High-volume processing
  • Video and audio input

Watch out

Superseded by 3.7 Flash on quality; still a solid mid-tier.

BalancedRecord checked September 3, 2026

Gemini 3.8 Flash

Gemini 3.8 Flash is Google's most-intelligent workhorse Flash — tied for the top of DeepSWE 1.1 with Claude Opus 5 at roughly a fifth of the cost per task, three Flash releases in six weeks.

Best for

  • Agentic coding at scale
  • Mid-difficulty engineering
  • Multimodal pipelines

Watch out

Intro pricing doubles on 2027-01-01, and Google says it can consume more tokens than 3.7 Flash — 3.7 Flash stays available for efficiency-first workloads.

Benchmark

DeepSWE 1.1 in context

Only Gemini 3.8 Flash has a published DeepSWE 1.1 result. It is shown against the wider field, with cost per completed task alongside the score.

Local leader

Gemini 3.8 Flash [high]

74%

Rows shown

27

Highest published reasoning effort per model (not best Pass@1)

Snapshot date

2026-09-03

Mirrored from deepswe.datacurve.ai

DeepSWE 1.1 pass@10%16%32%48%64%80%$0$4.50$9.00$13.50$18.00$22.50$27.00Avg cost per taskGemini 3.8 Flash [high]: 74% · $2.36 · 143k tokens · 166 stepsGemini 3.8 FlashClaude Opus 5 [max]: 74% · $11.84 · 118k tokens · 99 stepsClaude Opus 5GPT-5.6 Sol [max]: 73% · $8.39 · 60k tokens · 61 stepsGPT-5.6 SolClaude Fable 5 [max]: 70% · $21.63 · 119k tokens · 88 stepsClaude Fable 5GPT-5.6 Terra [max]: 70% · $4.95 · 72k tokens · 76 stepsGPT-5.6 TerraGLM 5.3 [max]: 69% · $3.99 · 80k tokens · 124 stepsGLM 5.3Kimi K3 [max]: 69% · $4.65 · 82k tokens · 98 stepsKimi K3GPT-5.6 Luna [max]: 67% · $3.03 · 73k tokens · 102 stepsGPT-5.6 LunaGPT-5.5 [xhigh]: 67% · $7.23 · 46k tokens · 82 stepsGPT-5.5Grok 4.6 [xhigh]: 67% · $5.50 · 71k tokens · 87 stepsGrok 4.6Gemini 3.7 Flash [high]: 65% · $2.18 · 107k tokens · 125 stepsGemini 3.7 FlashGLM 5.3 Flash [max]: 63% · $0.48 · 73k tokens · 123 stepsGLM 5.3 FlashDeepSeek V4-Pro [max]: 63% · $0.24 · 106k tokens · 155 stepsDeepSeek V4-ProClaude Opus 4.8 [max]: 59% · $13.22 · 135k tokens · 120 stepsClaude Opus 4.8Qwen3.8-Max [xhigh]: 58% · $3.73 · 95k tokens · 111 stepsQwen3.8-MaxMuse Spark 1.2 [xhigh]: 55% · $3.70 · 99k tokens · 101 stepsMuse Spark 1.2Claude Sonnet 5 [max]: 54% · $26.40 · 214k tokens · 268 stepsClaude Sonnet 5Grok 4.5 [high]: 54% · $2.42 · 36k tokens · 61 stepsGrok 4.5DeepSeek V4-Flash [max]: 53% · $0.10 · 108k tokens · 153 stepsDeepSeek V4-FlashMuse Spark 1.1 [xhigh]: 53% · $2.36 · 74k tokens · 96 stepsMuse Spark 1.1GPT-5.4 [xhigh]: 52% · $5.65 · 71k tokens · 70 stepsGPT-5.4Gemini 3.6 Flash [high]: 47% · $4.42 · 96k tokens · 117 stepsGemini 3.6 FlashGLM 5.2 [max]: 44% · $3.92 · 78k tokens · 129 stepsGLM 5.2Gemini 3.5 Flash [high]: 36% · $3.45 · 76k tokens · 105 stepsGemini 3.5 FlashKimi K2.7 Code: 31% · $2.82 · 59k tokens · 149 stepsKimi K2.7 CodeClaude Sonnet 4.6 [high]: 30% · $5.52 · 76k tokens · 134 stepsClaude Sonnet 4.6Gemini 3.1 Pro [high]: 12% · $2.14 · 28k tokens · 76 stepsGemini 3.1 Pro

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.

DeepSWE 1.1 leaderboard with pass rate, cost, tokens, and steps per task
#ModelPass@1Cost / taskTokens / taskSteps / task
1Gemini 3.8 Flash [high]74%$2.36143k166
2Claude Opus 5 [max]74%$11.84118k99
3GPT-5.6 Sol [max]73%$8.3960k61
4Claude Fable 5 [max]70%$21.63119k88
5GPT-5.6 Terra [max]70%$4.9572k76
6GLM 5.3 [max]69%$3.9980k124
7Kimi K3 [max]69%$4.6582k98
8GPT-5.6 Luna [max]67%$3.0373k102
9GPT-5.5 [xhigh]67%$7.2346k82
10Grok 4.6 [xhigh]67%$5.5071k87
11Gemini 3.7 Flash [high]65%$2.18107k125
12GLM 5.3 Flash [max]63%$0.4873k123
13DeepSeek V4-Pro [max]63%$0.24106k155
14Claude Opus 4.8 [max]59%$13.22135k120
15Qwen3.8-Max [xhigh]58%$3.7395k111
16Muse Spark 1.2 [xhigh]55%$3.7099k101
17Claude Sonnet 5 [max]54%$26.40214k268
18Grok 4.5 [high]54%$2.4236k61
19DeepSeek V4-Flash [max]53%$0.10108k153
20Muse Spark 1.1 [xhigh]53%$2.3674k96
21GPT-5.4 [xhigh]52%$5.6571k70
22Gemini 3.6 Flash [high]47%$4.4296k117
23GLM 5.2 [max]44%$3.9278k129
24Gemini 3.5 Flash [high]36%$3.4576k105
25Kimi K2.7 Code31%$2.8259k149
26Claude Sonnet 4.6 [high]30%$5.5276k134
27Gemini 3.1 Pro [high]12%$2.1428k76

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

Both charge $3.75 per million output tokens at standard rates, so output price is not the split. Prefer the row whose “best for” line matches the job, then confirm the live vendor rate.

Run the model picker

Evidence confidence: High

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

  • Pricing verified on both sidesInput and output rates are verified for both models.
  • 4/5 core specs verified on both sidesNot published for at least one side: parameter count.
  • 3 shared named benchmarks with differing scoresMeasured on: Artificial Analysis Intelligence Index, Terminal-Bench 2.1, Humanity's Last Exam.
  • Verified within the last 90 daysNewest catalog check was 6 days ago.
  • Both models carry source citationsEach side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

Common questions

Gemini 3.6 Flash vs Gemini 3.8 Flash

Answered from the verified figures on this page rather than general guidance.

Is Gemini 3.6 Flash or Gemini 3.8 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 3.8 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 3.8 Flash?

Gemini 3.8 Flash accepts 1.05M tokens against 1M for Gemini 3.6 Flash. This only matters if you routinely send very long documents or large codebases.

Do Gemini 3.6 Flash and Gemini 3.8 Flash support the same reasoning levels?

Yes — both accept the same effort settings: "low", "medium", "high". Higher effort costs more and takes longer, so start low and raise it only where output quality actually improves.

Should I use Gemini 3.6 Flash or Gemini 3.8 Flash?

Both sit in the balanced tier, so the choice usually comes down to price and context rather than capability. Gemini 3.6 Flash suits multimodal pipelines; Gemini 3.8 Flash suits agentic coding at scale.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Context: Gemini 3.8 Flash takes 1.05M against 1M for Gemini 3.6 Flash — only decisive if your prompts approach the smaller window.
  • Measured capability: Gemini 3.8 Flash leads Artificial Analysis Intelligence Index 58.7 to 54 (measured 2026-08-14).

Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.