Gemini 3.8 Flash vs GPT-5.4 Nano

Google DeepMind · US  |  OpenAI · US · Updated June 2026

Quick verdict

Pick Gemini 3.8 Flash for long-horizon agentic coding (deepswe v1.1) or cost-efficient workhorse performance beating larger models at same price as 3.7 flash. Pick GPT-5.4 Nano for cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work or classification, extraction, ranking and sub-agent execution at scale. On a tight budget at scale, GPT-5.4 Nano is the value pick.

Gemini 3.8 Flash (Google DeepMind) and GPT-5.4 Nano (OpenAI) are two of the models people most often weigh against each other in 2026. Gemini 3.8 Flash is google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research. GPT-5.4 Nano is openAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecGemini 3.8 FlashGPT-5.4 Nano
ProviderGoogle DeepMind (US) OpenAI (US)
ReleasedSeptember 2, 2026 March 17, 2026
Context window1M tokens (~1,500 pages) 400K (~600 pages)
Price (in/out)$0.75/$3.75 per 1M tokens $0.2/$1.25 per 1M tokens
Open weight?No — API only No — API only
Modalitiestext, image, audio, video text, image, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Long-horizon agentic coding (DeepSWE v1.1)

Gemini 3.8 Flash

Its 1M tokens window holds about 2.5× more than GPT-5.4 Nano's 400K in a single prompt.

Cost-efficient workhorse performance beating larger models at same price as 3.7 Flash

Gemini 3.8 Flash

Google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research — and it carries the larger 1M tokens context.

Vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%)

Gemini 3.8 Flash

Google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research — and it is the newer of the two.

Cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work

GPT-5.4 Nano

At $0.2/$1.25 per 1M tokens it undercuts Gemini 3.8 Flash ($0.75/$3.75 per 1M tokens), and that gap compounds at volume.

Classification, extraction, ranking and sub-agent execution at scale

GPT-5.4 Nano

OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning — and it runs cheaper at $0.2/$1.25 per 1M tokens.

A 400K context in the smallest, fastest GPT-5.4 variant

GPT-5.4 Nano

GPT-5.4 Nano lists a 400K context in the smallest, fastest GPT-5.4 variant among its strengths; Gemini 3.8 Flash does not.

Lowest cost at scale

GPT-5.4 Nano

At $0.2/$1.25 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Gemini 3.8 Flash

Its 1M tokens window is about 2.5× larger than GPT-5.4 Nano's 400K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ GPT-5.4 Nano

At $0.2/$1.25 per 1M tokens it undercuts Gemini 3.8 Flash, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Gemini 3.8 Flash

Larger 1M tokens window fits more in one prompt.

Anyone whose priority is long-horizon agentic coding (deepswe v1.1)

→ Gemini 3.8 Flash

It is specifically built for that.

Anyone whose priority is cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work

→ GPT-5.4 Nano

That is its strongest area.

Gemini 3.8 Flash: where it fits

Google's cost-efficient workhorse model released September 2, 2026, tuned for long-horizon coding and autonomous agents, launched alongside a restricted 'Cyber' variant for vulnerability research. Released September 2, 2026 by Google DeepMind, it is built for long-horizon agentic coding (DeepSWE v1.1), cost-efficient workhorse performance beating larger models at same price as 3.7 Flash, vals Finance Agent V2 benchmark (61.4%, ahead of Claude Opus 5's 58.6%); also leads a weak field on the Harvey legal benchmark (10.0%), and prompt-injection robustness (Gray Swan benchmark).

Its trade-offs are real: still a mid-tier 'Flash' model, not Google's frontier flagship (which remains unreleased), introductory price doubles on January 1, 2027, hLE-Verified score (54.9%) trails top frontier reasoning models, and built on the same base model as Gemini 3.7 Flash (a post-training update, not a freshly pretrained model). At $0.75 in / $3.75 out per million tokens, it sits in the budget price band.

GPT-5.4 Nano: where it fits

OpenAI's cheapest GPT-5.4 variant at $0.20/$1.25 with a 400K window — a speed-and-cost tier for high-volume tasks, not deep reasoning. Released March 17, 2026 by OpenAI, it is built for cheapest GPT-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work, classification, extraction, ranking and sub-agent execution at scale, a 400K context in the smallest, fastest GPT-5.4 variant, and text and image input for cheap multimodal pipelines.

Its trade-offs: a nano tier — not built for hard reasoning or frontier coding, no published SWE-Bench Verified score (OpenAI reported SWE-Bench Pro instead), outclassed by GPT-5.4 and GPT-5.4 Mini whenever a task needs real depth, and image input only — no audio or video. At $0.2 in / $1.25 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

Gemini 3.8 Flash and GPT-5.4 Nano overlap enough that the right pick depends on your specific job. GPT-5.4 Nano costs less per token; Gemini 3.8 Flash holds the larger context; and each leads in its own area — Gemini 3.8 Flash for long-horizon agentic coding (deepswe v1.1), GPT-5.4 Nano for cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work. Rather than crowning one, run the same hard task through both once and let the results decide.

Want both Gemini 3.8 Flash and GPT-5.4 Nano without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is Gemini 3.8 Flash or GPT-5.4 Nano better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 3.8 Flash leans toward long-horizon agentic coding (deepswe v1.1) while GPT-5.4 Nano leans toward cheapest gpt-5.4-family tier at $0.20/$1.25 — built for high-volume, latency-sensitive work, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Gemini 3.8 Flash or GPT-5.4 Nano?

GPT-5.4 Nano is cheaper — $0.75/$3.75 per 1M tokens vs $0.2/$1.25 per 1M tokens, roughly 3.8× apart on input.

Which has the bigger context window?

Gemini 3.8 Flash — 1M tokens vs 400K, about 2.5× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Gemini 3.8 Flash and GPT-5.4 Nano together?

Yes — a multi-model platform like LumiChats gives you Gemini 3.8 Flash, GPT-5.4 Nano and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, Gemini 3.8 Flash or GPT-5.4 Nano?

Gemini 3.8 Flash — released September 2, 2026, about 6 months after GPT-5.4 Nano.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.