Gemini 3.8 Flash vs GPT-6.1 Terra

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-6.1 Terra for $1/$5 per million tokens (cached input $0.05) - half the price of gpt-6.1 sol and gpt-5.6 terra or terminal-bench 4.0 of 50.3% at maximum reasoning effort, versus 21.5% for gpt-5.6 terra (openai-reported). On a tight budget at scale, Gemini 3.8 Flash is the value pick.

Gemini 3.8 Flash (Google DeepMind) and GPT-6.1 Terra (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-6.1 Terra is openAI's October 26, 2026 mid-tier model - a 1.05M-context, computer-use-capable GPT at $1/$5 per million tokens, half the price of its predecessor. 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-6.1 Terra
ProviderGoogle DeepMind (US) OpenAI (US)
ReleasedSeptember 2, 2026 October 26, 2026
Context window1M tokens (~1,500 pages) 1.05M tokens (~1,575 pages)
Price (in/out)$0.75/$3.75 per 1M tokens $1/$5 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

GPT-6.1 Terra is comparatively weak here — openAI still positions Sol and Astra as the higher-capability choices for demanding coding problems

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

Gemini 3.8 Flash

At $0.75/$3.75 per 1M tokens it undercuts GPT-6.1 Terra ($1/$5 per 1M tokens), and that gap compounds at volume.

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

GPT-6.1 Terra is comparatively weak here — benchmark figures are vendor-reported; no SWE-bench Verified or MRCR numbers published

$1/$5 per million tokens (cached input $0.05) - half the price of GPT-6.1 Sol and GPT-5.6 Terra

GPT-6.1 Terra

Gemini 3.8 Flash is comparatively weak here — introductory price doubles on January 1, 2027

Terminal-Bench 4.0 of 50.3% at maximum reasoning effort, versus 21.5% for GPT-5.6 Terra (OpenAI-reported)

GPT-6.1 Terra

Gemini 3.8 Flash is comparatively weak here — hLE-Verified score (54.9%) trails top frontier reasoning models

1.05M-token context with up to 128K output tokens, plus image input and computer use via the Responses API

GPT-6.1 Terra

OpenAI's October 26, 2026 mid-tier model - a 1.05M-context, computer-use-capable GPT at $1/$5 per million tokens, half the price of its predecessor — and it is the newer of the two.

Lowest cost at scale

Gemini 3.8 Flash

At $0.75/$3.75 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Which should you pick?

A cost-sensitive startup shipping high volume

→ Gemini 3.8 Flash

At $0.75/$3.75 per 1M tokens it undercuts GPT-6.1 Terra, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ GPT-6.1 Terra

Larger 1.05M 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 $1/$5 per million tokens (cached input $0.05) - half the price of gpt-6.1 sol and gpt-5.6 terra

→ GPT-6.1 Terra

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-6.1 Terra: where it fits

OpenAI's October 26, 2026 mid-tier model - a 1.05M-context, computer-use-capable GPT at $1/$5 per million tokens, half the price of its predecessor. Released October 26, 2026 by OpenAI, it is built for $1/$5 per million tokens (cached input $0.05) - half the price of GPT-6.1 Sol and GPT-5.6 Terra, terminal-Bench 4.0 of 50.3% at maximum reasoning effort, versus 21.5% for GPT-5.6 Terra (OpenAI-reported), 1.05M-token context with up to 128K output tokens, plus image input and computer use via the Responses API, and error rate on OpenAI's hard-prompt set falls from 53.6% (GPT-5.6 Terra) to 16.7% at low effort and 4.3% at max effort.

Its trade-offs: openAI still positions Sol and Astra as the higher-capability choices for demanding coding problems, requests above 272K input tokens are billed at 2x input and 1.5x output for the whole request, closed weights and no fine-tuning support, and benchmark figures are vendor-reported; no SWE-bench Verified or MRCR numbers published. At $1 in / $5 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

Gemini 3.8 Flash and GPT-6.1 Terra overlap enough that the right pick depends on your specific job. Gemini 3.8 Flash costs less per token; GPT-6.1 Terra holds the larger context; and each leads in its own area — Gemini 3.8 Flash for long-horizon agentic coding (deepswe v1.1), GPT-6.1 Terra for $1/$5 per million tokens (cached input $0.05) - half the price of gpt-6.1 sol and gpt-5.6 terra. 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-6.1 Terra 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-6.1 Terra 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-6.1 Terra leans toward $1/$5 per million tokens (cached input $0.05) - half the price of gpt-6.1 sol and gpt-5.6 terra, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Gemini 3.8 Flash or GPT-6.1 Terra?

Gemini 3.8 Flash is cheaper — $0.75/$3.75 per 1M tokens vs $1/$5 per 1M tokens, roughly 1.3× apart on input.

Which has the bigger context window?

Effectively neither — 1M tokens vs 1.05M tokens is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both Gemini 3.8 Flash and GPT-6.1 Terra together?

Yes — a multi-model platform like LumiChats gives you Gemini 3.8 Flash, GPT-6.1 Terra 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-6.1 Terra?

GPT-6.1 Terra — released October 26, 2026, about 54 days after Gemini 3.8 Flash.

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.