Comparison

GPT-5.4 vs Gemini 3.1 Pro in 2026: Coding, Agents, and 1M Context

EvoLink Team

Product Team

March 27, 2026

6 min read

If you are choosing between GPT-5.4 and Gemini 3.1 Pro, the weak version of this article asks which model is "better." The stronger version asks: which model is better for your specific production pattern: coding depth, agent tool use, computer use, multimodal analysis, or long-context cost?

As of March 27, 2026, official OpenAI and Google materials support a nuanced answer:

GPT-5.4 is the stronger fit when you care most about coding plus agent execution across tools and computer-use environments.
Gemini 3.1 Pro is the stronger fit when you want lower direct API cost, broader multimodal input support, and more published long-context evidence.

TL;DR

Choose GPT-5.4 for coding-heavy agents, computer-use workflows, and premium tool orchestration.
Choose Gemini 3.1 Pro for lower cost, multimodal input breadth, and more explicit public evidence around long-context behavior.
Do not declare a universal winner. The official numbers point to different strengths.

Verified snapshot

Model	What is clearly documented	Official pricing	Best fit
GPT-5.4	OpenAI positions it as the flagship frontier model for professional work, coding, tool use, and computer use, with `1M` context and `128K` max output	`$2.50/MTok` input, `$15/MTok` output	Coding agents, tool search, computer use, and professional task automation
Gemini 3.1 Pro	Google publishes a model card with multimodal input support, benchmark tables, and long-context eval signals, with `1M` context and `64K` max output	`$2/MTok` input, `$12/MTok` output up to `200K`; higher above `200K`	Cost-aware production workflows, multimodal analysis, and published long-context evaluation

Coding and agent benchmarks: strong, but not all apples-to-apples

This is where discipline matters. We should only compare benchmarks that are officially published and reasonably aligned.

Benchmark	GPT-5.4	Gemini 3.1 Pro	Takeaway
SWE-Bench Pro (Public)	`57.7%`	`54.2%`	GPT-5.4 has the edge on this specific published coding eval
BrowseComp	`82.7%`	`85.9%`	Gemini leads on published browsing eval
OSWorld-Verified	`75.0%`	not listed in the reviewed Google model card	GPT-5.4 has the clearer published computer-use story
MCP Atlas	not listed in the reviewed OpenAI article	`69.2%`	Gemini has clearer published MCP workflow evidence

The right conclusion is not that one model wins everything. It is that the evidence clusters by workload.

GPT-5.4's clearest advantages

OpenAI's March 5, 2026 release materials make three strengths unusually explicit:

native computer use
stronger tool selection and tool search
a flagship coding-and-agents positioning with 1M context and 128K output

If your workflow involves:

operating software through screenshots or UI tools
chaining multiple tools and connectors
writing, verifying, and iterating code with an agent loop

then GPT-5.4 is the better editorial recommendation.

Gemini 3.1 Pro's clearest advantages

Google's current model card gives Gemini 3.1 Pro clearer public support for:

multimodal inputs including text, image, audio, video, and large repositories
lower direct API pricing
explicit long-context evaluation data
published strength on Terminal-Bench 2.0 and MCP Atlas

That makes Gemini 3.1 Pro easier to recommend when:

multimodal developer workflows matter
cost sensitivity matters
you want more public evidence about long-context behavior before committing

Pricing and context: where Gemini gets the simpler cost story

Model	Standard pricing	Notes
GPT-5.4	`$2.50/MTok` input, `$15/MTok` output	OpenAI's flagship frontier pricing
Gemini 3.1 Pro up to `200K`	`$2/MTok` input, `$12/MTok` output	Lower listed cost at standard context
Gemini 3.1 Pro above `200K`	`$4/MTok` input, `$18/MTok` output	Still in the same general frontier range, but the cost gap narrows

Context also matters:

GPT-5.4 documents 1M context and 128K output.
Gemini 3.1 Pro documents 1M context and 64K output, and Google publishes MRCR v2 long-context numbers.

That does not make Gemini universally better at long-context work. It does mean Google publishes more direct long-context evidence in the reviewed sources.

A safer decision framework

If your main priority is...	Start with	Why
Coding agents that use tools and software environments	GPT-5.4	OpenAI's official materials make this the clearest strength
Native computer-use workflows	GPT-5.4	OpenAI publishes direct computer-use benchmark evidence
Lower direct API pricing	Gemini 3.1 Pro	Google's listed pricing is lower at standard context
Multimodal input breadth	Gemini 3.1 Pro	Google's model card documents broader modality coverage
Published long-context evidence	Gemini 3.1 Pro	Google publishes MRCR v2 signals directly
One premium model for professional coding plus agent work	GPT-5.4	The flagship positioning is strongest there

FAQ

Which model is better for coding?

The safer answer is workload-specific. GPT-5.4 looks stronger for agentic coding and computer-use workflows. Gemini 3.1 Pro looks stronger for lower-cost coding plus multimodal repository analysis.

Which model is cheaper?

Gemini 3.1 Pro is cheaper on current listed direct API pricing.

Which model has better published long-context evidence?

Gemini 3.1 Pro. Google's model card includes direct long-context evaluation signals.

Which model is better for tool-heavy agents?

GPT-5.4 is the safer answer because OpenAI's release materials emphasize tool search, agent workflows, and computer use.

Does GPT-5.4 support 1M context?

Yes. OpenAI's current model materials document 1M context.

What is the best production setup?

Many teams should route by job type: GPT-5.4 for tool-heavy coding agents and Gemini 3.1 Pro for lower-cost multimodal analysis and long-context runs.

Compare Both Models on EvoLink

If you want to test GPT-5.4 and Gemini 3.1 Pro behind one API layer, EvoLink is the practical way to compare routing behavior and real workload cost without maintaining separate provider integrations.

Compare Coding Models on EvoLink