Head to head
Gemini 3.8 Flash vs DeepSeek-V4.1-Flash
Gemini 3.8 Flash from Google DeepMind against DeepSeek-V4.1-Flash from DeepSeek — specification, price and every benchmark both makers have published, in one table.
At a glance
Benchmarks
Evenly split
Across the 2 benchmarks both models report, they are level — Gemini 3.8 Flash takes 1 and DeepSeek-V4.1-Flash takes 1. Which one is better depends entirely on which test resembles your work.
Price
DeepSeek-V4.1-Flash is cheaper
On a 3:1 input-to-output mix, DeepSeek-V4.1-Flash costs $0.525 per million tokens against $1.50 for Gemini 3.8 Flash — about 2.9× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.
What actually differs
- These two are closely matched on the specification side — same broad capabilities, same licensing posture. The decision comes down to the benchmark rows and the price.
Scorecard
Which is better at what
Maths, coding, reasoning and the rest — one line each, averaged over the benchmarks both models actually report.
| Category | Gemini 3.8 Flash | DeepSeek-V4.1-Flash | Better at this |
|---|---|---|---|
| ReasoningGPQA Diamond | 95.3% | 90.9% | Gemini 3.8 Flash+4.4 |
| MathsNeither model reports this | — | — | Not comparable |
| CodingTerminal-Bench 2.1 | 89.4% | 90.6% | DeepSeek-V4.1-Flash+1.2 |
| KnowledgeNeither model reports this | — | — | Not comparable |
| MultimodalNeither model reports this | — | — | Not comparable |
| Instruction followingNeither model reports this | — | — | Not comparable |
| Human preferenceNeither model reports this | — | — | Not comparable |
| Categories wonOut of 2 comparable | 1 | 1 | Split — Gemini 3.8 Flash and DeepSeek-V4.1-Flash |
Each category averages only the benchmarks every model here reports, so no one is credited for a test the other did not run. A category with no shared test is marked Not comparable rather than guessed at.
Side by side
Gemini 3.8 Flash and DeepSeek-V4.1-Flash, row by row
| Attribute | Gemini 3.8 FlashGoogle DeepMind | DeepSeek-V4.1-FlashDeepSeek |
|---|---|---|
| Specification | ||
| MakerWho built it | Google DeepMind | DeepSeek |
| Released | 2026-09 | 2026-09 |
| ParametersTotal, and active per token for a mixture of experts | Not reported | 552B |
| Architecture | Not reported | MoE |
| Context windowHow much can go in at once | 1,048,576 tokens | 1,048,576 tokens |
| Max output | 65,536 tokens | 393,216 tokens |
| Input | Text, Image, Audio, Video | Text, Image |
| ReasoningSpends extra tokens thinking before it answers | Yes | Not reported |
| Tool calling | Yes | Yes |
| Licence | Proprietary | Not stated |
| Open weightsCan you download and run it yourself | No | Not reported |
| Price | ||
| Input priceUSD per million tokens in | $0.75 | $0.30 |
| Output priceUSD per million tokens out | $3.75 | $1.20 |
| Cached input | Not reported | $0.0060 |
| Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone | $1.50 | $0.525Cheapest |
| Published benchmarks | ||
| GPQA Diamond198 graduate-level physics, chemistry and biology questions written to be Google-proof. PhD holders in the matching field score about 65%. | ||
| SWE-bench ProA harder, contamination-resistant successor to SWE-bench Verified, drawn from commercial and copyleft repositories that were never public training data. | Not reported | |
| Terminal-Bench 2.1The 2.1 revision of the terminal agent benchmark. Scores on it are not comparable with Terminal-Bench 2.0 or 4.0 — the task set changed. | ||
| Links | Full page | Full page |
| Row verified | 2026-10 | 2026-10 |
Where these numbers come from
Every score on this page is a published figure, taken from the model's own card, system card, technical report or release post, or from a public leaderboard. CorX Labs did not run these evaluations. Most are self-reported by the lab that built the model, which means they were produced under that lab's own choice of prompt, scaffold and number of attempts — so treat them as a starting point for a shortlist, not as a settled ranking.
A score someone other than the model's maker measured is marked Independent and names its measurer. Those are the stronger numbers on this page — an outside harness has no reason to flatter anyone — and there are not many of them.
Where a figure has not been published, the cell reads Not reported rather than an estimate. Nothing here is inferred, interpolated or guessed. Each model records the month its row was last checked. Full method and caveats.
Questions
Gemini 3.8 Flash or DeepSeek-V4.1-Flash?
Is Gemini 3.8 Flash better than DeepSeek-V4.1-Flash?
Across the 2 benchmarks both models report, they are level — Gemini 3.8 Flash takes 1 and DeepSeek-V4.1-Flash takes 1. Which one is better depends entirely on which test resembles your work.
Which is cheaper, Gemini 3.8 Flash or DeepSeek-V4.1-Flash?
On a 3:1 input-to-output mix, DeepSeek-V4.1-Flash costs $0.525 per million tokens against $1.50 for Gemini 3.8 Flash — about 2.9× less. Remember that a reasoning model bills its thinking as output, so cost per answer can diverge much further than cost per token.
What is the difference between Gemini 3.8 Flash and DeepSeek-V4.1-Flash?
These two are closely matched on the specification side — same broad capabilities, same licensing posture. The decision comes down to the benchmark rows and the price.
