GPT-5.5 vs Gemini 3.1 Pro: an honest head-to-head for builders

GPT-5.5 vs Gemini 3.1 Pro: which one should you ship on right now?

GPT-5.5 vs Gemini 3.1 Pro, in one breath: GPT-5.5 is the stronger engineering agent — it leads agentic and terminal coding and is the only one that reliably recalls across a 1M-token window. Gemini 3.1 Pro is the value-and-reasoning pick — roughly 2.5x cheaper, strong on science, and natively multimodal with audio and video in. Route by task, not by leaderboard.

Why only these two right now?

If you’d asked me three months ago which model I’d put on a hard coding agent, I might have said Claude Fable 5 — it still tops the coding charts (SWE-Bench Pro 80.3%). So why isn’t it in this fight?

Here’s the verified context, kept short: on June 12, 2026 a US export-control directive suspended Fable 5 and Mythos 5 for any foreign national. Because nationality can’t be verified at the API layer, Anthropic disabled both models for every customer worldwide — and as of late June 2026 they remain offline with no restoration confirmed (Anthropic’s notice). I’m a builder in Puebla; I can’t ship on a model my whole region can’t call.

So I’m not going to center this post on what we can’t use. The real choice for builders today is OpenAI’s GPT-5.5 versus Google’s Gemini 3.1 Pro — the two frontier models you can actually put in production right now. I’ve run both. Let me tell you where each one genuinely wins, plainly and without the hype.

What are GPT-5.5 and Gemini 3.1 Pro?

GPT-5.5: OpenAI, released April 23, 2026. It powers ChatGPT and Codex, OpenAI’s agentic coding tool. Context window is 1,000,000 tokens (400,000 inside Codex), with a max output of 128K. It’s the engineering-flavored release — and it’s notably token-efficient, which matters more than it sounds. Primary source: OpenAI’s GPT-5.5 announcement.

Gemini 3.1 Pro: Google, released in preview February 19, 2026. It’s everywhere across Google’s stack — Gemini API / AI Studio, Gemini CLI, Vertex AI, Gemini Enterprise, the Gemini app, and NotebookLM. Context window is also 1,000,000 tokens, max output 64K. The headline differentiator: it’s natively multimodal — text, image, audio, and video input. Primary source: Google’s Gemini 3.1 Pro post.

Gemini’s launch credentials were no joke either: ARC-AGI-2 at 77.1% (more than double Gemini 3 Pro), GPQA Diamond at 94.3% (a record at the time), and it topped 13 of 16 tracked benchmarks at launch.

Both are real frontier models. Neither is a toy. The rest of this post is about finding where each one earns its place on your stack.

Which one writes and ships code better?

This is where I spend most of my day, so let me be precise — and keep the benchmarks distinct, because they get conflated constantly.

Benchmark comparisons report:

• SWE-Bench Pro — the harder multi-file resolution benchmark — GPT-5.5 58.6% vs Gemini 54.2% (+4.4 for GPT-5.5).
• SWE-Bench Verified — a distinct, easier benchmark; do not conflate it with Pro — GPT-5.5 82.6% vs Gemini 80.6% (+2.0).
• Terminal-Bench 2.0 — real CLI/terminal agentic workflows — GPT-5.5 82.7% vs Gemini 68.5% (+14.2). That’s the widest coding gap in the whole comparison.

That terminal gap is the one I feel in practice. When an agent is chaining shell commands, reading errors, and editing files across a repo, GPT-5.5 holds the thread better.

But give Gemini its due: on competitive programming, LiveCodeBench Pro puts Gemini around 2,887 Elo (GPT-5.5 not published) — a genuine strength. That’s a different skill from agentic engineering: tight, self-contained algorithmic problems where Gemini’s reasoning shines.

OPINION: for production agentic coding and Codex-style terminal workflows, GPT-5.5 is the one I reach for. Gemini is no slouch at raw code generation — I just trust GPT-5.5 more when the work spans a real repo. (I went deeper on GPT-5.5’s coding chops versus the now-suspended Fable 5 in this post.)

Coding head-to-head (REPORTED benchmark comparisons)

SWE-Bench Pro — GPT-5.558.6%

SWE-Bench Pro — Gemini 3.1 Pro54.2%

Terminal-Bench 2.0 — GPT-5.582.7%

Terminal-Bench 2.0 — Gemini 3.1 Pro68.5%

Who reasons better — and who can see and hear?

This is Gemini’s home turf, and I’ll call it fairly.

On scientific reasoning, it’s effectively a tie: GPQA Diamond is Gemini 94.3% vs GPT-5.5 93.6%. Don’t pick a model on a 0.7-point delta. Gemini does pull ahead on ARC-AGI-2 at 77.1% — more than double its predecessor — and on web browsing, BrowseComp at 85.9% is a real Gemini strength.

The clean differentiator, though, is multimodal. Gemini 3.1 Pro is natively multimodal: it accepts audio and video input, not just text and images. GPT-5.5 has no equivalent video-in story in what I’m working from. If your product needs to reason over a video clip or a voice recording at the model layer, that’s not a benchmark argument — it’s a capability the other model simply doesn’t have.

OPINION: if your product reasons over science, browses the live web, or ingests audio/video, Gemini is the natural fit. I’d default to it for those routes without much hand-wringing.

What does each actually cost?

Here are the verified list prices, per 1M tokens.

GPT-5.5: $5 input / $30 output (Standard, ≤200K context); GPT-5.5 Pro $30 / $180; batch about $2.50 / $15.

Gemini 3.1 Pro: $2 input / $12 output (≤200K context), stepping to $4 / $18 above 200K; batch about $1 / $6.

Across every tier, Gemini lands roughly 2.5x cheaper. The worked example makes it concrete: at 100M output tokens per month, Gemini runs about $1,200 versus GPT-5.5’s roughly $3,000.

OPINION: that ~$1,800/month delta is real money at scale. Cost alone can — and should — decide your high-volume, lower-stakes routes. I do this math before I commit to a model, not after; if you want the framework, I wrote up the token-cost math here.

Output price per 1M tokens (VERIFIED list pricing, ≤200K context)

GPT-5.5 output / 1M$30

Gemini 3.1 Pro output / 1M$12

Both say 1M context — but can they actually recall it?

This is the section I most want builders to read, because the spec sheet lies to you here.

Both models advertise a 1,000,000-token context window. Identical on paper. But advertised window is not the same as reliable recall — and the gap is enormous. On MRCR v2 retrieval at the full 1M context, GPT-5.5 recalls 74.0% versus Gemini’s 26.3% — a 47.7-point gap. You can stuff a million tokens into either, but only one of them reliably finds the needle deep in that haystack.

A few more specs that matter when your workload is genuinely large: max output is GPT-5.5 128K vs Gemini 64K, and GPT-5.5’s Codex window is 400K. On tool orchestration, GPT-5.5 posts MCP Atlas 75.3%. And on token-efficiency, GPT-5.5 uses about 40% fewer output tokens than GPT-5.4 on the same Codex tasks — which quietly narrows the per-task cost gap on coding work, even though Gemini’s headline price is lower.

OPINION: if your workload actually fills the context — big repos, long documents, deep multi-step agents — recall is the spec that decides everything, and here GPT-5.5 is in a different league. A model that “supports 1M” but recalls a quarter of it is not the same product.

The 1M-context recall gotcha

Advertised context (both)1,000,000 tokens

Recall @1M — GPT-5.574.0%

Recall @1M — Gemini 3.1 Pro26.3%

Gap47.7 points

FAQ: quick answers for builders

Which is better for an agentic coding tool? GPT-5.5. It leads Terminal-Bench (82.7% vs 68.5%) and SWE-Bench Pro (58.6% vs 54.2%).

Which is cheaper? Gemini 3.1 Pro, about 2.5x across every tier ($12 vs $30 output per 1M).

Which handles audio and video input? Only Gemini 3.1 Pro is natively multimodal with audio and video in.

Both claim 1M context — same thing? No. Recall at the full 1M is 74% (GPT-5.5) vs 26.3% (Gemini); only GPT-5.5 reliably recalls across the whole window.

Why isn’t Claude Fable 5 here? It’s currently suspended under a US export-control directive and offline for all customers as of late June 2026.

Best for scientific reasoning? Roughly tied on GPQA Diamond, 94.3% vs 93.6%, with Gemini ahead on ARC-AGI-2 at 77.1%.

The verdict: route by task

I don’t pick one model for everything. I route — and after running both in real workloads, here’s how I split them.

Pick GPT-5.5 for agentic and terminal coding, multi-file resolution, genuine long-context recall, tool orchestration, the bigger 128K output, and token-efficiency. It’s the stronger engineering agent.

Pick Gemini 3.1 Pro for cost (~2.5x cheaper), scientific reasoning, competitive programming, web browsing, and native audio + video. It’s the stronger value, reasoning, and multimodal pick.

Where each model genuinely wins

GPT-5.5 — the engineering agent

• Agentic + terminal coding (Terminal-Bench 82.7%)
• Multi-file resolution (SWE-Bench Pro 58.6%)
• Genuine 1M recall (74% vs 26.3%)
• Tool orchestration (MCP Atlas 75.3%)
• Bigger 128K output + token-efficient

Gemini 3.1 Pro — value + reasoning + multimodal

• ~2.5x cheaper ($12 vs $30 output / 1M)
• Scientific reasoning (ARC-AGI-2 77.1%)
• Competitive programming (LiveCodeBench ~2,887 Elo)
• Web browsing (BrowseComp 85.9%)
• Native audio + video input

In production I send heavy engineering and long-context agents to GPT-5.5, and high-volume, reasoning, browsing, and multimodal work to Gemini. That routing — plus hard cost caps — is most of how I keep an AI product both good and affordable; I wrote up how I wire it together in running LLMs in production, with more in my AI agents guides.

The one rule that really matters: test on your task, not the leaderboard. The benchmarks above are a starting map, not your answer. The right model is the one that wins your specific job at your budget — so wire up both, run your actual workload through them, and let the results decide.