💰 Unit Cost Winner Gemini 3.8 Flash $0.25/1M in · $1.50/1M out
⚡ Latency & Speed Winner Gemini 3.8 Flash 105ms TTFT · 210 tok/s
💻 Coding (SWE) Winner GPT-6 Astra 75.7% SWE-bench · 96.8% HumanEval
🧠 STEM Reasoning Winner GPT-6 Astra 96% GPQA · 98% MATH-500

Which model is better: Gemini 3.8 Flash or GPT-6 Astra?

Gemini 3.8 Flash is 97.5% cheaper for input tokens ($0.25 vs. $10.00 per 1M tokens) and $1.50 vs. $50.00 for output tokens (40.0x cost advantage). In terms of operational performance, Gemini 3.8 Flash delivers lower response latency at 105ms TTFT (635ms faster than GPT-6 Astra) with 210 vs. 74 tok/s throughput. GPT-6 Astra leads coding evaluations at 75.7% SWE-bench Verified vs. 68.5% for Gemini 3.8 Flash and 96.0% vs. 79.2% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.

Verified Performance & Unit Economics Delta (12 Dimensions)

Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.

Metric / Dimension Gemini 3.8 Flash GPT-6 Astra Net Delta / Advantage
Input Cost / 1M Tokens $0.25 $10.00 Gemini 3.8 Flash is 40.0x cheaper
Output Cost / 1M Tokens $1.50 $50.00 Gemini 3.8 Flash (97.0% lower)
Prompt Cache Read / 1M $0.025 $1.000 Gemini 3.8 Flash is 40.0x cheaper
Prompt Cache Write / 1M $0.25 $12.50 Gemini 3.8 Flash is 50.0x cheaper
Context Window (Max Output) 1,049k (66k out) 1,075k (128k out) GPT-6 Astra (1.0x larger context)
Avg TTFT Response Latency 105ms 740ms Gemini 3.8 Flash (635ms faster)
Generation Speed (Throughput) 210 tok/s 74 tok/s Gemini 3.8 Flash (+136 tok/s faster)
SWE-bench Verified (Coding) 68.5% 75.7% GPT-6 Astra (+7.2% lead)
GPQA Diamond (PhD STEM) 79.2% 96% GPT-6 Astra (+16.8% lead)
MMLU-Pro (Multi-Discipline) 87.4% 91.2% GPT-6 Astra (+3.8% lead)
MATH-500 (Quantitative Logic) 95.1% 98% GPT-6 Astra (+2.9% lead)
Inference Value Score (IVR) 74 / 100 47.6 / 100 Gemini 3.8 Flash (+26.4 pts)

Interactive Monthly Token Economics & ROI Forecaster

Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.

25.0M Tokens
500K 50M 250M 500M+
5.0M Tokens
100K 25M 75M 150M+
50% Cached
0% (Uncached) 25% 50% (Typical Agent) 95% Max

Estimated Monthly Spend

Gemini 3.8 Flash $10.94

$0.25/1M in · $1.50/1M out

Prompt Cache: $0.025/1M read · $0.25/1M write

GPT-6 Astra $387.50

$10.00/1M in · $50.00/1M out

Prompt Cache: $1.000/1M read · $12.50/1M write

Projected Monthly Cost Reduction
$376.56 / mo
(97.2% lower cost with Gemini 3.8 Flash)
Open Full Matchup Analysis →
A

When to Choose Gemini 3.8 Flash

Choose Gemini 3.8 Flash (Google) for high-throughput agentic workflows, real-time multimodal RAG, live search grounding, and production code generation at flash unit economics — offering a 1,048k context window, 105ms TTFT, and $0.25/1M input pricing ($0.025/1M cached reads).

B

When to Choose GPT-6 Astra

Choose GPT-6 Astra (OpenAI) for high-autonomy flagship reasoning model engineered for end-to-end computer and browser use, advanced scientific research, and complex multi-turn software engineering — offering a 1,075k context window, 740ms TTFT, and $10.00/1M input pricing ($1.000/1M cached reads).

Related Questions & Decision Factors

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

Gemini 3.8 Flash is cheaper at $0.25/1M input tokens ($1.50/1M output) compared to $10.00/1M input ($50.00/1M output) for GPT-6 Astra.

Which model has lower TTFT latency and higher tok/s throughput: Gemini 3.8 Flash or GPT-6 Astra?

Gemini 3.8 Flash delivers faster first-token latency at 105ms TTFT (210 tok/s throughput) compared to 740ms TTFT (74 tok/s) for GPT-6 Astra.

How do Gemini 3.8 Flash and GPT-6 Astra compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, Gemini 3.8 Flash scores 68.5% versus 75.7% for GPT-6 Astra. On GPQA Diamond graduate-level STEM reasoning, Gemini 3.8 Flash scores 79.2% versus 96% for GPT-6 Astra (and 95.1% vs 98% on MATH-500).

When should you choose Gemini 3.8 Flash?

Choose Gemini 3.8 Flash (Google) for high-throughput agentic workflows, real-time multimodal RAG, live search grounding, and production code generation at flash unit economics — offering a 1,048k context window, 105ms TTFT, and $0.25/1M input pricing ($0.025/1M cached reads).

When should you choose GPT-6 Astra?

Choose GPT-6 Astra (OpenAI) for high-autonomy flagship reasoning model engineered for end-to-end computer and browser use, advanced scientific research, and complex multi-turn software engineering — offering a 1,075k context window, 740ms TTFT, and $10.00/1M input pricing ($1.000/1M cached reads).

Explore High-Priority Related Model Matchups