Gemini 3.8 Flash vs GPT-6 Astra

Why this pair: OpenAI's flagship versus the current Google model included in its launch comparisons

Benchmark Performance

Available Benchmarks

No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.
FieldAt a Glance
Google DeepMind · activeGemini 3.8 FlashVerified Sep 2, 2026
OpenAI · activeGPT-6 AstraVerified Sep 4, 2026

Technical Differences

Side-by-Side Facts

CuratedIndexable
FieldGemini 3.8 FlashGPT-6 Astra
DeveloperGoogle DeepMindOpenAI
FamilyGemini 3Gpt 6
ModelGemini 3.8 FlashGPT-6 Astra
VersionGemini 3.8 FlashGPT-6 Astra
Lifecycleactiveactive
Released2026-09-022026-09-03
Knowledge cutoffUnknown2026-04-30
Input modalitiesText, Image, Video, Audio, DocumentText, Image
Output modalitiesTextText
Context window1,049K1,050K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessGoogle AI (Standard), Google Gemini (Standard)Openai (Standard), Openrouter (Standard)
Capabilitieschat, code_execution, computer_use, generation, reasoning, structured_outputs, toolschat, computer-use, generation, reasoning, research, tools

14 comparable fields · 9 material differences · Editorially curated pair passes the primary-source comparison gate

Gemini 3.8 Flash Capabilities

chatcode executioncomputer usegenerationreasoningstructured outputstools
Input price$0.75
Output price$3.75
Serving providers2
Canonical IDgoogle-deepmind/gemini-3.8-flash

GPT-6 Astra Capabilities

chatcomputer-usegenerationreasoningresearchtools
Input price$10.00
Output price$50.00
Serving providers2
Canonical IDopenai/gpt-6-astra

Internal Comparison Graph

Related Comparisons

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Primary Evidence

Sources and Freshness

Questions

Gemini 3.8 Flash vs GPT-6 Astra FAQs

Is Gemini 3.8 Flash or GPT-6 Astra better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.8 Flash and GPT-6 Astra, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini 3.8 Flash or GPT-6 Astra?+

Gemini 3.8 Flash is $0.75 and GPT-6 Astra is $10.00 per million tokens, so Gemini 3.8 Flash is cheaper on this metric. Gemini 3.8 Flash is $3.75 and GPT-6 Astra is $50.00 per million tokens, so Gemini 3.8 Flash is cheaper on this metric.

Which has a larger context window, Gemini 3.8 Flash or GPT-6 Astra?+

GPT-6 Astra has the larger sourced context window. Gemini 3.8 Flash supports 1,049K and GPT-6 Astra supports 1,050K.

Which performs better in benchmarks, Gemini 3.8 Flash or GPT-6 Astra?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can Gemini 3.8 Flash or GPT-6 Astra be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Gemini 3.8 Flash is not marked open weight; GPT-6 Astra is not marked open weight.

Can Gemini 3.8 Flash and GPT-6 Astra understand images?+

Gemini 3.8 Flash is documented with image input; GPT-6 Astra is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini 3.8 Flash or GPT-6 Astra?+

GPT-6 Astra has the larger sourced maximum output: Gemini 3.8 Flash supports 66K and GPT-6 Astra supports 128K output tokens.

Do Gemini 3.8 Flash and GPT-6 Astra support reasoning and tool use?+

Gemini 3.8 Flash: reasoning, tool calling, and image input. GPT-6 Astra: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini 3.8 Flash or GPT-6 Astra?+

Gemini 3.8 Flash has 2 sourced provider routes; GPT-6 Astra has 2, a tie.

Which offers better value, Gemini 3.8 Flash or GPT-6 Astra?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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