Back to Blog
GeminiAI ModelsBenchmarksCoding AIGoogle AI

Gemini 3.1 Pro vs Opus 4.6 and GPT-5.2: Benchmarks

7 min read

Did Gemini 3.1 Pro Just Leapfrog Opus 4.6 and GPT-5.2?

Gemini 3.1 Pro is a genuinely strong model that competes with Opus 4.6 and GPT-5.2 on most major benchmarks — and undercuts both on price. It topped the Terminal-Bench 2.0 leaderboard at 68.5 (among independently verified scores), tied Opus 4.6 on the 128K needle-in-a-haystack test, and beat GPT-5.2 on output cost. That's not nothing. But "competing" isn't the same as "winning," and if you're already locked into a coding workflow built around Claude Code or GPT-5.3-Codex, this probably isn't the release that makes you pack your bags.

Here's what the numbers actually mean, where Gemini 3.1 Pro holds up, and where it still trails.

How Does Gemini 3.1 Pro Pricing Compare to Opus 4.6 and GPT-5.2?

This is actually where Gemini 3.1 Pro's case is strongest, so let's start here.

For input tokens (under 200K), Gemini 3.1 Pro runs $2 per million tokens — meaningfully cheaper than Opus 4.6 at $5 and slightly above GPT-5.2. On the output side, Gemini 3.1 Pro is $12 per million tokens versus Opus 4.6 at $25. That's more than a 50% discount on output compared to Anthropic's flagship.

GPT-5.2's output pricing lands higher than Gemini 3.1 Pro as well, so if you're doing high-volume work where output tokens add up fast — agentic workflows, long code generation tasks, document processing — Gemini is the cheapest of the three flagship options.

The bottom line on price: Gemini 3.1 Pro is the most affordable frontier model in this comparison, and it's not particularly close on output costs.

Note that Gemini supports up to a 1 million token context window, and pricing does step up beyond 200K tokens. But for most use cases — and most users — the sub-200K tier is where you'll live.

How Does Gemini 3.1 Pro Perform on Coding Benchmarks?

This is the interesting part. Let's go through the benchmarks that actually matter.

Terminal-Bench 2.0

Terminal-Bench 2.0 tests coding ability inside a terminal environment — multi-turn, complex tasks that simulate real agentic work. Gemini 3.1 Pro scored 68.5, which is the best independently verified score on this benchmark right now. GPT-5.3-Codex did score higher in its self-reported results (77.3), but the independently tested number was 64.7 — lower than Gemini. That gap between self-reported and independently verified is worth paying attention to.

Is it slightly awkward that Google is essentially doing the same thing by highlighting their own benchmarks? Sure. But a 68.5 on Terminal-Bench is impressive regardless.

SWE-Bench Verified

This benchmark gives the model a real GitHub issue from a repo it hasn't seen before, with problems verified by humans. Gemini 3.1 Pro scored 80.6, which is a solid improvement from 76.2 on Gemini 3 Pro. Opus 4.6 edges it slightly at 80.8. GPT-5.2 comes in at 80. So: roughly a three-way tie at the top, with Gemini just barely behind Opus.

SWE-Bench Pro

Same concept, harder problems. Gemini 3.1 Pro scores 54.2. Anthropic doesn't run this test for Opus 4.6, so there's no comparison there. GPT-5.3-Codex beats it at 56.8. So Gemini leads the non-coding-specialist models here but trails OpenAI's dedicated coding model.

How Good Is Gemini 3.1 Pro at Long Context Tasks?

Gemini's 1 million token context window is one of its flagship selling points. So the question is: can it actually use all that context?

The answer is mixed.

On Browser Comp — where the model is given hundreds of documents and articles and has to synthesize answers across all of them — Gemini 3.1 Pro shows strong performance. This is a real-world proxy for things like processing large codebases, analyzing research corpora, or summarizing across many files.

The MRCR (multi-needle) test is more revealing. This is the "needle in a haystack" test on steroids — multiple pieces of information buried in a massive document, and the model has to retrieve all of them accurately.

  • At 128K context: Gemini 3.1 Pro scored 84.9, tying Opus 4.6 and beating GPT-5.2. GPT-5.3-Codex couldn't attempt it at this context length.
  • At 1 million context: Gemini 3.1 Pro scored 26.3 — which is rough, but also the only model that could even attempt the test at that scale. No other model in this comparison can handle 1M tokens at all.

So here's the honest take on Gemini's long-context capabilities: up to 128K, it's competitive with anyone. Beyond that, it's the only game in town — but performance degrades significantly. For most practical use cases, the 128K performance is the number that matters.

What Can You Actually Build with Gemini 3.1 Pro Right Now?

Google showed off some demos, and I'll give them credit — a few were genuinely impressive.

The side-by-side SVG generation comparison between Gemini 3 Pro and 3.1 Pro showed a clear visual improvement in output quality. They also demoed a 3D ISS orbital tracker built with a single prompt, and honestly it looked pretty good.

Over on Reddit, someone used Gemini 3.1 Pro inside Canvas — Google's in-browser coding environment — to build an entire space exploration game. Zero external tools. That's the kind of thing that would have been a hard demo even 6 months ago.

Take all demos with appropriate skepticism. But the underlying capability for visual output, UI generation, and interactive coding does appear to be a genuine step up from Gemini 3 Pro.

Where Can You Access Gemini 3.1 Pro?

Gemini 3.1 Pro is available now in several places:

  • Google AI Studio (API access required)
  • Google Antigravity (Google's developer tooling environment)
  • Vertex AI and Gemini Enterprise (for cloud/enterprise users)
  • The Gemini web app (for everyday users)
  • NotebookLM

If you're a developer, Google AI Studio and Antigravity are your fastest on-ramps. If you're a regular user, it's rolling out in the Gemini app now.

Should You Switch to Gemini 3.1 Pro from Claude or GPT?

Honest answer: probably not if you're already deep in a specific workflow.

Here's the thing — Gemini 3.1 Pro puts Google back on even footing with Anthropic and OpenAI. That's genuinely good news. A few months ago, Gemini 3 launched and it really did feel like it had pulled ahead of the field. This update is more like Gemini catching up after falling slightly behind, rather than lapping the competition again.

If you're solidly in the Claude Code camp, the benchmark delta between Opus 4.6 and Gemini 3.1 Pro isn't big enough to justify switching your entire workflow. Same story for GPT-5.3-Codex users — Codex still edges Gemini on the SWE-Bench Pro numbers.

Where Gemini 3.1 Pro makes a compelling case:

  • You're price-sensitive and doing high output-token volume work
  • You need large context windows (128K–1M range) and no other model supports that for your use case
  • You're already a Gemini user and this is a meaningful upgrade for your current setup
  • You want strong visual/SVG/UI generation capabilities alongside coding

More good models in the market is always a win for us as users. Competition keeps pricing down and capability up. Gemini 3.1 Pro is a real competitor, and that matters even if it isn't rewriting the leaderboard.

Frequently Asked Questions

How much does Gemini 3.1 Pro cost compared to Claude Opus 4.6?

Gemini 3.1 Pro costs $2 per million input tokens and $12 per million output tokens (under 200K context). Claude Opus 4.6 costs $5 per million input tokens and $25 per million output tokens. Gemini 3.1 Pro is significantly cheaper on both input and output, making it the more cost-effective choice for high-volume usage.

Is Gemini 3.1 Pro better than GPT-5.2 for coding?

It depends on the benchmark. On Terminal-Bench 2.0, Gemini 3.1 Pro (68.5) outperforms GPT-5.2's independently verified scores. On SWE-Bench Verified, they're close — Gemini scores 80.6 versus GPT-5.2 at 80. On SWE-Bench Pro (the harder version), GPT-5.3-Codex leads at 56.8 versus Gemini's 54.2. Overall, they're roughly peer models for general coding tasks, with Gemini slightly cheaper on output.

What is Terminal-Bench 2.0 and why does it matter?

Terminal-Bench 2.0 is a benchmark that tests an AI model's coding ability inside a terminal environment, using multi-turn, complex tasks that simulate real agentic software workflows. It's considered a more realistic measure of coding performance than single-shot tests because it requires the model to navigate iterative problem-solving — closer to how developers actually use AI coding tools.

What is the needle-in-a-haystack test for AI models?

The needle-in-a-haystack test embeds a specific piece of information somewhere inside a very large document and asks the model to retrieve it accurately. The MRCR (multi-needle) variant does this with multiple hidden pieces of information simultaneously, which is significantly harder. It's used to evaluate how well a model actually uses its context window at scale, not just whether it accepts long inputs.

Where is Gemini 3.1 Pro available right now?

Gemini 3.1 Pro is available via the Gemini API in Google AI Studio, Google Antigravity, and Gemini CLI for developers. It's also rolling out in Vertex AI and Gemini Enterprise for cloud users, and in the Gemini web app and NotebookLM for everyday users.

If you want to go deeper into AI model comparisons and how to build with these tools, join the free Chase AI community for templates, prompts, and live breakdowns. And if you're serious about building with AI, check out the paid community, Chase AI+ for hands-on guidance on how to make money with AI.