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Gemini 3.8 Flash vs DeepSeek V4.1-Flash

Google DeepMind against DeepSeek, on sourced numbers only. Green marks the higher (or cheaper) value on each row; the text below only calls a lead when the gap is bigger than measurement noise.

Gemini 3.8 FlashDeepSeek V4.1-Flash
Arena Elo1496 source ↗1476 source ↗
GPQA Diamond95.4% source ↗90.9% source ↗
$ per 1M in / out$0.75 / $3.75 source ↗$0.15 / $0.60 source ↗
Blended $ per 1M (3:1)$1.50 source ↗$0.26 source ↗
Context window1M source ↗1M source ↗
Output speed117 tok/s source ↗174 tok/s source ↗
ReleasedSep 2, 2026 source ↗Sep 10, 2026 source ↗
Open weightsNo source ↗Yes source ↗
Reasoning effort tierslow · medium · high (default: medium)low · high · max (default: high)
API effort controlthinking_config={"thinking_level": "low" | "medium" | "high"} source ↗thinking={"type": "enabled" | "disabled"}, reasoning_effort="low" | "high" | "max" (medium and xhigh map to high) source ↗
Additional benchmarksDeepSWE v1.1: 73.7% source ↗DeepSWE v1.1: 77.0% (Oct 17 update; 74.2% on Sep 10) source ↗
Best for (workload)95.4% on GPQA Diamond (Epoch AI) and 117 tok/s output (Artificial Analysis) at a Flash-tier price. source ↗Fastest output we track (174 tok/s, Artificial Analysis) and the lowest list price ($0.15 / $0.60 off-peak); MIT open weights. source ↗

Arena Elo from the arena.ai text leaderboard as of Oct 27, 2026 source ↗. Speed measured by Artificial Analysis and varies by provider.

Which one should I use?

For high-volume work where cost matters most, DeepSeek V4.1-Flash is about 5.7 times cheaper per token. For overall answer quality as rated by people in blind tests, Gemini 3.8 Flash leads clearly (1496 vs 1476 Arena Elo). For hard science and analysis questions, Gemini 3.8 Flash scores higher on GPQA Diamond (95.4% vs 90.9%). For latency-sensitive use such as live chat, DeepSeek V4.1-Flash is noticeably faster (174 vs 117 tokens per second). If you need to self-host or fine-tune, only DeepSeek V4.1-Flash has open weights. On release timing, Google DeepMind is at day 24 of a typical ~32-day gap (MID-CYCLE), and DeepSeek is at day 20 of a typical ~32-day gap (MID-CYCLE).

Key differences from the sourced numbers

Frequently asked questions

Is Gemini 3.8 Flash or DeepSeek V4.1-Flash better?

DeepSeek V4.1-Flash is the cheaper option: $0.26 vs $1.50 per 1M tokens at a 3:1 input-to-output mix (about 5.7 times less). Gemini 3.8 Flash scores 20 Elo points higher (1496 vs 1476) on the Arena text leaderboard, where people vote on blind side-by-side answers. On GPQA Diamond, graduate-level science questions, Gemini 3.8 Flash leads 95.4% to 90.9%. DeepSeek V4.1-Flash streams output faster: 174 vs 117 tokens per second (Artificial Analysis). Gemini 3.8 Flash pricing note: Until Dec 31, 2026; $1.50 / $7.50 from Jan 1, 2027. No higher rate for long prompts. DeepSeek V4.1-Flash pricing note: Off-peak price (cache hit $0.003 / 1M). Peak hours (01:00 to 04:00 and 06:00 to 10:00 UTC, weekdays): $0.30 / $1.20 (cache hit $0.006 / 1M).

Which one should I use: Gemini 3.8 Flash or DeepSeek V4.1-Flash?

For high-volume work where cost matters most, DeepSeek V4.1-Flash is about 5.7 times cheaper per token. For overall answer quality as rated by people in blind tests, Gemini 3.8 Flash leads clearly (1496 vs 1476 Arena Elo). For hard science and analysis questions, Gemini 3.8 Flash scores higher on GPQA Diamond (95.4% vs 90.9%). For latency-sensitive use such as live chat, DeepSeek V4.1-Flash is noticeably faster (174 vs 117 tokens per second). If you need to self-host or fine-tune, only DeepSeek V4.1-Flash has open weights. On release timing, Google DeepMind is at day 24 of a typical ~32-day gap (MID-CYCLE), and DeepSeek is at day 20 of a typical ~32-day gap (MID-CYCLE).

How much does Gemini 3.8 Flash cost compared with DeepSeek V4.1-Flash?

Gemini 3.8 Flash lists at $0.75 / $3.75 per 1M input/output tokens ($1.50/1M blended at 3:1). DeepSeek V4.1-Flash lists at $0.15 / $0.60 per 1M input/output tokens ($0.26/1M blended at 3:1).

Google DeepMind lab hub →DeepSeek lab hub → Compare by effort level & use case → Compare any two models

Facts checked Nov 5, 2026 against official sources. Every number links to its source. Spotted an error? Email [email protected].