Model comparison

Gemini 3.8 Flash vs GPT-6 Luna

Google against OpenAI, compared on context, price, and verified benchmark results.

Catalog record checked September 26, 2026Individual provider fields may changeEvidence confidence: High — see receipts below

Google

Gemini 3.8 Flash

Balanced

OpenAI

GPT-6 Luna

Budget

AI model capability comparison
Specification	Gemini 3.8 Flash	GPT-6 Luna
Provider
Provider	Google	OpenAI
Tier
Tier	Balanced	Budget
Context window
Context window	1.05M	Winner: 1.05M
Max output
Max output	66K	Winner: 128K
Input / 1M tokens
Input / 1M tokens	$0.75	Winner: $0.10
Output / 1M tokens
Output / 1M tokens	$3.75	Winner: $0.50
Weights
Weights	Closed	Closed
Parameters
Parameters	Not disclosedUnverified	GPT-6 low-cost tier
Reasoning levels
Reasoning levels	low, medium, high	none, low, medium, high, xhigh, max
Modalities
Modalities	text, image, video, audio, pdf	text, image
API model id
API model id	gemini-3.8-flash	gpt-6-luna
Released
Released	September 2, 2026	September 22, 2026
Artificial Analysis Intelligence Index [high] (2026-09-26)
Artificial Analysis Intelligence Index [high] (2026-09-26)	40.9	Not verifiedUnverified
Artificial Analysis Intelligence Index [max] (2026-09-26)
Artificial Analysis Intelligence Index [max] (2026-09-26)	Not verifiedUnverified	37.3
Terminal-Bench 2.1 (2026-09-02)
Terminal-Bench 2.1 (2026-09-02)	89.4	Not verifiedUnverified
Humanity's Last Exam (2026-09-02)
Humanity's Last Exam (2026-09-02)	54.9	Not verifiedUnverified
DeepSWE 1.1 [max] (2026-09-22)
DeepSWE 1.1 [max] (2026-09-22)	Not verifiedUnverified	66.6

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

Where each score comes from, and how far it can be compared across models.

2026-09-26
Artificial Analysis
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check measuredAt.
2026-09-22
OpenAI — Introducing GPT-6 Sol and Luna (vendor; not yet on Datacurve's board)
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
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.
Comparable with caveatSubset/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.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

Pricing tiers

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.

GPT-6 Luna: Direct successor to GPT-5.6 Luna at half the price. Cached input $0.01/MTok; cache writes $0.125. Above 272K input the whole request bills $0.20/$0.75 ($0.02 cached). Batch/Flex $0.05/$0.25; Fast mode $0.20/$1.00. Knowledge cutoff May 18 2026.

BalancedRecord checked September 26, 2026

Gemini 3.8 Flash

Gemini 3.8 Flash is Google's most capable workhorse Flash — within a point of the top of DeepSWE 1.1 at a fraction of frontier cost per task.

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.

BudgetRecord checked September 26, 2026

GPT-6 Luna

GPT-6 Luna is OpenAI's cheapest current model at $0.10/$0.50 — the direct successor to GPT-5.6 Luna at half the price, with the full 1.05M-token context.

Best for

High-volume chat and extraction
Classification
Cost-capped agent loops

Watch out

Small tier: it matches GPT-5.6 Luna on the Artificial Analysis index but sits well below GPT-6 Sol on hard reasoning; the DeepSWE figure is vendor-reported at max effort.

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

GPT-6 Astra [xhigh]

74%

Rows shown

Best published Pass@1 per model (Datacurve's default view)

Snapshot date

2026-09-26

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.

DeepSWE 1.1 leaderboard with pass rate, cost, tokens, and steps per task
#	Model	Pass@1	Cost / task	Tokens / task	Steps / task
1	GPT-6 Astra [xhigh]	74%	$4.43	30k	29
2	Gemini 3.8 Flash [high]	74%	$2.36	143k	166
3	Claude Opus 5 [max]	74%	$11.84	118k	99
4	GPT-5.6 Sol [max]	73%	$8.39	60k	61
5	Claude Fable 5 [xhigh]	70%	$13.41	80k	68
6	GPT-5.6 Terra [max]	70%	$4.95	72k	76
7	GLM 5.3 [max]	69%	$3.99	80k	124
8	Kimi K3 [max]	69%	$4.65	82k	98
9	Grok 4.6 [medium]	68%	$3.45	50k	70
10	GPT-5.6 Luna [max]	67%	$3.03	73k	102
11	GPT-5.5 [xhigh]	67%	$7.23	46k	82
12	Gemini 3.7 Flash [medium]	66%	$2.03	94k	117
13	GLM 5.3 Flash [max]	63%	$0.48	73k	123
14	DeepSeek V4-Pro [max]	63%	$0.24	106k	155
15	Claude Opus 4.8 [max]	59%	$13.22	135k	120
16	Qwen3.8-Max [xhigh]	58%	$3.73	95k	111
17	Muse Spark 1.2 [xhigh]	55%	$3.70	99k	101
18	Claude Sonnet 5 [max]	54%	$26.40	214k	268
19	Grok 4.5 [high]	54%	$2.42	36k	61
20	DeepSeek V4-Flash [max]	53%	$0.10	108k	153
21	Muse Spark 1.1 [xhigh]	53%	$2.36	74k	96
22	GPT-5.4 [xhigh]	52%	$5.65	71k	70
23	Gemini 3.6 Flash [high]	47%	$4.42	96k	117
24	GLM 5.2 [max]	44%	$3.92	78k	129
25	Gemini 3.5 Flash [high]	36%	$3.45	76k	105
26	Kimi K2.7 Code	31%	$2.82	59k	149
27	Claude Sonnet 4.6 [high]	30%	$5.52	76k	134
28	Gemini 3.1 Pro [high]	12%	$2.14	28k	76

“Best per model” shows each model’s highest published Pass@1, as Datacurve’s own board does; switch to all effort levels to see every configuration. Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.

When the cheaper one wins

GPT-6 Luna is cheaper on output at $0.50 per million tokens against $3.75 for Gemini 3.8 Flash — about 7.5×. 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. These are standard-tier API rates, excluding batch and cache discounts.

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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 sides — Input and output rates are verified for both models.
4/5 core specs verified on both sides — Not published for at least one side: parameter count.
1 shared named benchmark with differing scores — Measured on: Artificial Analysis Intelligence Index.
Verified within the last 90 days — Newest catalog check was 2 days ago.
Both models carry source citations — Each 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.

Gemini 3.8 Flash: Google — Gemini 3.8 Flash and 3.8 Flash Cyber (accessed 2026-09-03)
Gemini 3.8 Flash: Gemini API pricing (gemini-3.8-flash $0.75/$3.75 intro) (accessed 2026-09-03)
Gemini 3.8 Flash: DeepSWE 1.1 leaderboard — gemini-3.8-flash [high] 73.8% at $2.36/task (accessed 2026-09-03)
GPT-6 Luna: OpenAI — Introducing GPT-6 Sol and Luna (accessed 2026-09-26)
GPT-6 Luna: OpenAI model docs — gpt-6-luna (1.05M context, 128K output) (accessed 2026-09-26)
GPT-6 Luna: OpenAI API pricing (gpt-6-luna $0.10/$0.50, cached $0.01) (accessed 2026-09-26)

Related comparisons

Diving deeper on one model? Gemini 3.8 Flash · GPT-6 Luna

Common questions

Gemini 3.8 Flash vs GPT-6 Luna

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

Is Gemini 3.8 Flash or GPT-6 Luna cheaper for input?

GPT-6 Luna is cheaper at $0.10 per million input tokens, against $0.75 for Gemini 3.8 Flash — roughly 7.5× 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 3.8 Flash has tiered pricing: 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. GPT-6 Luna has tiered pricing: Direct successor to GPT-5.6 Luna at half the price. Cached input $0.01/MTok; cache writes $0.125. Above 272K input the whole request bills $0.20/$0.75 ($0.02 cached). Batch/Flex $0.05/$0.25; Fast mode $0.20/$1.00. Knowledge cutoff May 18 2026.

Is Gemini 3.8 Flash or GPT-6 Luna cheaper for output?

GPT-6 Luna is cheaper at $0.50 per million output tokens, against $3.75 for Gemini 3.8 Flash — roughly 7.5× 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 3.8 Flash has tiered pricing: 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. GPT-6 Luna has tiered pricing: Direct successor to GPT-5.6 Luna at half the price. Cached input $0.01/MTok; cache writes $0.125. Above 272K input the whole request bills $0.20/$0.75 ($0.02 cached). Batch/Flex $0.05/$0.25; Fast mode $0.20/$1.00. Knowledge cutoff May 18 2026.

Which has the larger context window, Gemini 3.8 Flash or GPT-6 Luna?

Both accept about 1.05M tokens of context, so document length will not decide between them.

Do Gemini 3.8 Flash and GPT-6 Luna support the same reasoning levels?

Gemini 3.8 Flash exposes low, medium, high, while GPT-6 Luna exposes none, low, medium, high, xhigh, max.

Should I use Gemini 3.8 Flash or GPT-6 Luna?

Gemini 3.8 Flash is the balanced tier and GPT-6 Luna the budget tier. The useful question is whether your hardest task actually fails on the cheaper one — most production volume such as classification, extraction and summarisation does not.

Next step

Choosing between them

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

Input price: GPT-6 Luna costs $0.10 per 1M tokens versus $0.75 for Gemini 3.8 Flash — a 7.5x difference at the headline tier.
Context: GPT-6 Luna takes 1.05M against 1.05M for Gemini 3.8 Flash — only decisive if your prompts approach the smaller window.
Measured capability: Gemini 3.8 Flash leads Artificial Analysis Intelligence Index 40.9 to 37.3 (measured 2026-09-26).
Positioning: Gemini 3.8 Flash sits in the balanced tier, GPT-6 Luna in the budget tier — most production volume (classification, extraction, summarisation) does not need the pricier tier.

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