Gemini 3.8 Flash vs GPT-6 Astra
At a Glance
| Compare |  Gemini 3.8 FlashGoogle DeepMind |  GPT-6 AstraOpenAI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v4.4 | #4 of 5790.0 score · 3/3 sources · complete | #6 of 5784.6 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | #17 of 27$0.160 per LiveBench case | #26 of 27$0.520 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.9 | #7 of 2758.5 score · 3/3 sources · complete | #18 of 2746.1 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.75Google AI ↗ · Sep 2, 2026 | $10.00Openai ↗ · Sep 4, 2026 |
| Output priceFrom · USD / 1M tokens | $3.75Google AI ↗ · Sep 2, 2026 | $50.00Openai ↗ · Sep 4, 2026 |
| Context windowMaximum documented tokens | 1,049K | 1,050K |
| Model facts checked | Sep 2, 2026View model evidence → | Sep 4, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | Gemini 3.8 Flash | GPT-6 Astra |
|---|---|---|
| ARC-AGI-1verified-v1-302d8267c052 · verified_score · statistical tie | 98.50100% of row best · percent · Gemini 3.8 Flash (High) | 98.50100% of row best · percent · GPT-6 Astra (XHigh) |
| ARC-AGI-2verified-v2-bca68e12cc9a · verified_score · leader | 89.1794% of row best · percent · Gemini 3.8 Flash (High) | 95.00100% of row best · percent · GPT-6 Astra (Max) |
| LMArena Vision Arenavision-2026-10-09-a64ca3b12b6e · arena_rating · leader | 1,314.08100% of row best · rating · gemini-3.8-flash-high; 95% CI [1303.54395703, 1324.61562115]; votes 3792; rank 8 | 1,281.3598% of row best · rating · gpt-6-astra-max; 95% CI [1270.63592513, 1292.07239183]; votes 3590; rank 36 |
| LiveBench2026-06-25-90f8d44da965 · overall · leader | 80.8594% of row best · percent · gemini-3.8-flash-high · 42,786 output tokens / case | 86.47100% of row best · percent · gpt-6-astra-max · 10,407 output tokens / case |
| ToneBench2026-10-17-10-task-e9c76358afb2 · overall_score · unverified | 70.2285% of row best · points · Gemini 3.8 Flash (high thinking) · 20,369 output tokens / case | 82.32100% of row best · points · GPT-6 Astra (max) · 20,449 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 1 tie | 1 benchmark win | 2 benchmark winsOverall lead |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Quality Versus Estimated Output Cost
Side-by-Side Facts
| Field | Gemini 3.8 Flash | GPT-6 Astra |
|---|---|---|
| Developer | Google DeepMind | OpenAI |
| Family | Gemini 3 | Gpt 6 |
| Model | Gemini 3.8 Flash | GPT-6 Astra |
| Version | Gemini 3.8 Flash | GPT-6 Astra |
| Lifecycle | active | active |
| Released | 2026-09-02 | 2026-09-03 |
| Knowledge cutoff | Unknown | 2026-04-30 |
| Input modalities | Text, Image, Video, Audio, Document | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,050K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Google AI (Standard), Google Gemini (Standard) | Openai (Standard), Openrouter (Standard) |
| Capabilities | chat, code_execution, computer_use, generation, reasoning, structured_outputs, tools | chat, computer-use, generation, reasoning, research, tools |
Gemini 3.8 Flash Capabilities
GPT-6 Astra Capabilities
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Gemini 3.8 FlashGoogle DeepMind | vs |  Qwen3.8-FlashQwen | cross-developer peerstext |
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 1,048,576 tokens and GPT-6 Astra supports 1,050,000 tokens.
Which performs better in benchmarks, Gemini 3.8 Flash or GPT-6 Astra?+
GPT-6 Astra leads the current overall benchmark count. The result uses 4 protocol-matched benchmarks from 3 publishers; it is not a universal quality score.
Can Gemini 3.8 Flash or GPT-6 Astra be self-hosted?+
Both models have the same sourced 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 65,536 tokens and GPT-6 Astra supports 128,000 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.