Gemini 3.1 Pro vs GPT-5: Honest Comparison for Developers (2026)

Hey fellow AI tinkerers — if you've spent the last two weeks just trying to keep up with model releases, same. February 2026 has been genuinely chaotic. Google dropped Gemini 3.1 Pro on February 19. OpenAI pushed GPT-5.3-Codex on February 5. Anthropic shipped Opus 4.6 in between. I'm Hanks, and my job is pretty simple: put these things inside real workflows, break them, and tell you what actually happened. Not demos. Real work.

This week's question: if you're building agentic systems or shipping production code, do you pick Gemini 3.1 Pro or one of the GPT-5 variants? I ran both through the same tasks. Here's what I found.

The Models Being Compared

Before anything else — let's be precise about what we're actually comparing. "GPT-5" is not one model in 2026. There's the general-purpose GPT-5.2 (xhigh), and there's the coding-specialized GPT-5.3-Codex, released February 5, 2026. On the Google side, Gemini 3.1 Pro dropped February 19, 2026, as an upgraded checkpoint of the Gemini 3 series.

These are meaningfully different tools. Treating them as the same model is where most comparison articles go wrong.

Sources: Google DeepMind model card, OpenAI's GPT-5.3-Codex launch post, Awesome Agents pricing breakdown.

Where Gemini 3.1 Pro Wins

Reasoning and Agentic Tasks (ARC-AGI-2, APEX, MCP Atlas)

This is the part that genuinely surprised me. Gemini 3.1 Pro scored 77.1% on ARC-AGI-2 — more than double Gemini 3 Pro's score of 31.1%. ARC-AGI-2 matters because it specifically tests novel pattern recognition, not memorized knowledge. You can't train your way to a high score on it in the traditional sense. GPT-5.2 sits well behind here.

On the APEX-Agents leaderboard, which Mercor CEO Brendan Foody described as measuring "real professional tasks," Gemini 3.1 Pro took the top spot immediately after launch. The MCP Atlas score of 69.2% means multi-tool coordination — databases, APIs, services talking to each other — holds up under real-world conditions.

For agentic workflow builders, that last number is the one to watch. If your pipeline chains more than three tools together, Gemini 3.1 Pro is currently the most reliable option on multi-step coordination.

On GPQA Diamond (graduate-level science reasoning), Gemini 3.1 Pro hits 94.3%, compared to GPT-5.3-Codex's 73.8%. For research-adjacent tasks, this gap is hard to ignore.

Context Window: 1M Tokens vs GPT-5's Limit

Gemini 3.1 Pro's 1 million token context window isn't a marketing number — it's architecturally load-bearing for certain use cases. Enterprise legal review, analyzing an entire codebase in a single call, synthesizing across large document sets: these workflows are genuinely different when your model doesn't hit a ceiling at 400K tokens.

Worth noting from DataCamp's February 2026 analysis: Gemini's MoE (Mixture-of-Experts) architecture delivers near-perfect recall on needle-in-a-haystack tests, but cost doubles for contexts over 200K tokens. If you're hitting that range regularly, prompt design and context caching become essential line items, not optional.

For front-end work specifically, Gemini 3.1 Pro holds the #1 WebDev Arena ranking at 1,443 Elo. React components, CSS from descriptions, design-mockup-to-code — this is its strongest coding lane.

Price: Gemini Wins Clearly at Scale

Let's just put the numbers side by side.

At high API volumes, Gemini 3.1 Pro is roughly 1.75–2.3× cheaper than GPT-5.3-Codex and dramatically cheaper than Opus 4.6. The batch API for GPT-5.3-Codex ($1.75/$14.00) brings asynchronous workloads closer to parity, but for synchronous pipelines, Gemini's pricing advantage is real. At scale, this is not a small difference.

Where GPT-5.3-Codex Wins

Terminal-Bench 2.0 and CLI-Focused Coding

This is GPT-5.3-Codex's home territory, and it earns it. The model scored 77.3% on Terminal-Bench 2.0, up from 64.0% in GPT-5.2-Codex — a jump that OpenAI's official launch post describes as a major step toward general-purpose agentic execution. Terminal-Bench 2.0 specifically tests multi-step agent execution: file editing, git operations, running unit tests, iteratively fixing bugs without human intervention.

The practical read: if your workflow lives in a terminal — running CI pipelines, managing deployment scripts, debugging via CLI — GPT-5.3-Codex is the stronger tool right now. Gemini 3.1 Pro's equivalent figure sits lower on this specific benchmark.

GPT-5.3-Codex also scored 77.6% on cybersecurity CTF benchmarks, which is relevant for security audits and exploit detection workflows. OpenAI classifies it as the first model rated "High" capability in cybersecurity under their Preparedness Framework — both a capability claim and a reason to read their safety documentation before deploying it in sensitive environments.

One genuinely useful feature: GPT-5.3-Codex supports mid-task steering. You can ask questions, redirect, or reprioritize while the model is actively running. For long-horizon tasks where you want to supervise without constantly restarting, this is a real workflow improvement over models that require full re-runs.

# Example: GPT-5.3-Codex via OpenAI API (agentic task with tool use)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-5.3-codex",
    messages=[
        {"role": "user", "content": "Find all deprecated API calls in this repo and open a fix PR for each."}
    ],
    tools=[{"type": "code_interpreter"}],
)

SWE-Bench Pro Specialized Results

On SWE-Bench Pro — OpenAI's preferred benchmark that spans four programming languages and is designed to be contamination-resistant — GPT-5.3-Codex scores 56.8%, edging GPT-5.2-Codex's 56.4%. This isn't a dramatic gap, but it keeps GPT-5.3-Codex at the top of this specific leaderboard.

SWE-Bench Pro is harder and more diverse than the original SWE-Bench Verified. The multi-language scope matters if your production codebase mixes Python, JavaScript, Go, and Rust — which is most production codebases.

The counterpoint: on SWE-Bench Verified (the Python-only original benchmark), Claude Opus 4.6 still leads at ~80.6%, with Gemini 3.1 Pro close behind. The three-way race on bug-fixing is genuinely tight, and which model "wins" depends heavily on which version of the benchmark you're running.

Which Should You Pick?

General Reasoning and Agentic Workflows → Gemini 3.1 Pro

If you're building multi-tool pipelines, working with large document sets, doing research-heavy tasks, or need the most cost-efficient frontier model at scale — Gemini 3.1 Pro is the cleaner choice right now. It leads on abstract reasoning, holds the top agentic leaderboard spot, and costs significantly less per token. The 1M context window is genuinely useful for anyone processing large codebases or long documents in a single pass.

Access it immediately through Google AI Studio or the Gemini API via Vertex AI. It's also now in public preview in GitHub Copilot.

Terminal-Heavy Coding Work → GPT-5.3-Codex

If your work centers on autonomous terminal execution — managing CI/CD, debugging via CLI, security auditing, long-horizon software tasks where you want to steer mid-run — GPT-5.3-Codex is built specifically for this. The 77.3% Terminal-Bench 2.0 score represents real operational capability, not benchmark gaming.

The practical trade-off: you pay more per token, and API access is still in gradual rollout as of late February 2026. For interactive use, the Codex app and CLI are available on paid ChatGPT plans.

The smartest move if you're unsure: run a PoC with your actual task mix. Track human interventions per task, not just success/failure rates. That's the number that predicts real cost.

At Macaron, we see this exact tension every day — developers choosing between models mid-workflow, context getting lost when switching between tools, and AI plans that never quite make it from conversation to execution. If you want to test how a real task holds up across these models without rebuilding your workflow from scratch, try running one inside Macaron — you can keep your context intact and judge the output yourself.

Frequently Asked Questions

Is Gemini 3.1 Pro generally available? As of February 26, 2026, it's in preview. Google has indicated GA is coming soon. It's available now via Google AI Studio, Gemini API, Vertex AI, Gemini CLI, GitHub Copilot, and the Gemini app for Pro/Ultra subscribers.

Does GPT-5.3-Codex have API access? As of early launch (February 5, 2026), API access was in gradual rollout. OpenAI described it as "being safely enabled." Check the OpenAI API documentation for current status.

Which model is better at SWE-Bench? On SWE-Bench Verified (Python), Claude Opus 4.6 and Gemini 3.1 Pro are close at ~80%+. On SWE-Bench Pro (multi-language), GPT-5.3-Codex leads at 56.8%. These are different benchmarks — pick the one that matches your actual language stack.

Is the 1M context window actually useful? For most tasks: no. For specific use cases — full codebase analysis, long legal/financial documents, multi-session research synthesis — it's genuinely useful and removes a painful architectural constraint.

How do I choose if I need both reasoning and coding? The honest answer is: test both on your actual tasks. For a team without time to run a PoC, Gemini 3.1 Pro is the safer default — broader benchmark leadership, lower cost, and a stable context window advantage.