Best AI Models 2026: Gemini 3.1 Pro vs Claude Opus 4.6 vs GPT-5.2 — Full Comparison

On February 19, 2026, Google dropped Gemini 3.1 Pro — and it immediately shook up the AI model rankings. Within 24 hours of release, it claimed the top spot on multiple benchmarks that matter to developers: ARC-AGI-2, GPQA Diamond, BrowseComp, and LiveCodeBench Pro.

But here's the thing: no single model wins everywhere. Claude Opus 4.6 still leads on SWE-Bench Verified. GPT-5.2 dominates SWE-Bench Pro. Sonnet 4.6 has the highest agentic workflow Elo. And Qwen3.5 is an open-weight beast with 397 billion parameters that you can run yourself.

If you're a developer trying to figure out which AI model to use in February 2026, this comparison cuts through the hype. We've pulled every number from official model cards and pricing pages — no training data, no guesswork. Let's break it down.

Quick Comparison: All 6 Models at a Glance

Before we dive into individual benchmarks, here's the high-level view. This table covers the specs that matter most when you're picking a model for production use.

A few things jump out immediately. Gemini 3.1 Pro has the largest context window at 1 million tokens and the richest multimodal input support (including audio and video). DeepSeek V3.2 is absurdly cheap at $0.28 per million input tokens. And Claude Opus 4.6 is the most expensive option by a wide margin — it better deliver on benchmarks to justify that 7.5x price premium over GPT-5.2's input costs.

Reasoning Benchmarks: Who Thinks Best?

Reasoning is where frontier models separate themselves from the pack. We're looking at three key benchmarks here: HLE (Humanity's Last Exam), GPQA Diamond (graduate-level science questions), and ARC-AGI-2 (abstract reasoning).

HLE (Humanity's Last Exam) — No Tools

HLE is one of the hardest benchmarks out there — questions designed to stump AI systems. Here's how the models perform without any tool access:

Gemini 3.1 Pro takes the crown at 44.4%, a full 4.4 points above Claude Opus 4.6. GPT-5.2 lands at 34.5% — surprisingly behind both Google and Anthropic's top models. Qwen3.5 trails at 28.7%, which is understandable given it's an open-weight model with only 17 billion active parameters in its MoE architecture.

HLE with Search and Code Tools

When models get access to search and code execution tools, the picture shifts:

Interesting reversal. Claude Opus 4.6 pulls ahead at 53.1% when it can use tools — suggesting Anthropic's model is particularly good at knowing when and how to leverage external resources. Gemini 3.1 Pro is close behind at 51.4%. GPT-5.2 still lags at 45.5%.

GPQA Diamond (Graduate-Level STEM)

All models score above 88% here, but Gemini 3.1 Pro edges everyone at 94.3%. GPT-5.2 takes second at 92.4% — this is clearly one of OpenAI's stronger areas. The spread is only ~6 points across all five models, meaning GPQA Diamond is becoming less useful for differentiating frontier models.

ARC-AGI-2 (Abstract Reasoning)

ARC-AGI-2 tests novel pattern recognition — the kind of reasoning that's hardest to brute-force with scale. This is where Gemini 3.1 Pro absolutely dominates:

A 77.1% on ARC-AGI-2 is remarkable. Gemini 3.1 Pro beats Claude Opus 4.6 by over 8 points and GPT-5.2 by a staggering 24 points. If abstract reasoning matters for your use case — say, novel problem solving or creative code architecture — Gemini 3.1 Pro is the clear winner.

Coding Benchmarks: The Developer's Bottom Line

For most developers reading this article, coding performance is probably what you care about most. Let's look at the benchmarks that actually measure real-world coding ability.

SWE-Bench Verified (Single Attempt)

SWE-Bench Verified tests whether a model can fix real GitHub issues in a single attempt. This is the gold standard for coding capability:

This is incredibly tight at the top. Claude Opus 4.6 leads at 80.8%, but Gemini 3.1 Pro is just 0.2% behind at 80.6%. GPT-5.2 and Sonnet 4.6 are effectively tied with the leaders. The real story here is that the top four models are within 1.2 points of each other — SWE-Bench Verified is becoming saturated at the frontier.

SWE-Bench Pro (Single Attempt)

SWE-Bench Pro is harder than Verified and separates the models more clearly:

OpenAI takes the lead here with GPT-5.2 at 55.6% and the specialized Codex variant at 56.8%. Gemini 3.1 Pro is close at 54.2%. Unfortunately, Anthropic didn't report SWE-Bench Pro scores for Claude models in the sources we verified, so we can't include them here.

LiveCodeBench Pro (Elo Rating)

LiveCodeBench Pro measures competitive programming ability using an Elo system:

Gemini 3.1 Pro's Elo of 2887 absolutely crushes GPT-5.2's 2393. That's a nearly 500-point gap — in Elo terms, that's a massive skill difference. If you're doing competitive programming or complex algorithmic work, Gemini 3.1 Pro is in a league of its own.

Terminal-Bench 2.0 (Terminus-2)

Terminal-Bench tests real terminal-based coding tasks:

Gemini 3.1 Pro leads again at 68.5%. Claude Opus 4.6 takes a strong second at 65.4%. Note how base GPT-5.2 (54.0%) is significantly weaker than the Codex variant (64.7%) — if you're using OpenAI for coding, make sure you're using the right model.

SciCode (Scientific Computing)

Another Gemini 3.1 Pro win. For scientific computing tasks, it leads by 7 points over both Claude Opus 4.6 and GPT-5.2.

Agentic Benchmarks: Who Works Best Autonomously?

Agentic capabilities — how well a model operates autonomously with tools, APIs, and multi-step workflows — are increasingly important for production applications. Here's where things get really interesting.

APEX-Agents

Gemini 3.1 Pro leads significantly at 33.5%. This benchmark is still relatively new, and scores across the board are low — but the 10+ point gap over GPT-5.2 is notable.

GDPval-AA (Agentic Automation Elo)

This Elo-based benchmark measures how well models perform in agentic automation scenarios:

Plot twist: Claude Sonnet 4.6 has the highest Elo here at 1633 — even beating the more expensive Opus 4.6 (1606). And Gemini 3.1 Pro, despite dominating everywhere else, comes in last at 1317. This is a significant gap — 316 Elo points below Sonnet 4.6.

This matters a lot. If you're building agentic workflows — think autonomous code review systems, multi-step data pipelines, or AI-driven DevOps — the Claude models are substantially better than Gemini or GPT for this specific pattern.

τ2-bench (Customer Service Automation)

For customer service scenarios, Claude models and Gemini 3.1 Pro are all bunched at the top. GPT-5.2 notably lags in the Retail category at 82.0% — nearly 10 points behind the leaders.

MCP Atlas (Tool Use)

Gemini 3.1 Pro leads MCP Atlas (tool-use benchmark) at 69.2%, nearly 8 points clear. Interestingly, Sonnet 4.6 beats Opus 4.6 here too — further evidence that Anthropic's mid-tier model punches above its weight for agentic tasks.

OSWorld-Verified (Computer Use)

For full computer-use scenarios (navigating GUIs, clicking buttons, filling forms), Claude Opus 4.6 dominates at 66.3%. Qwen3.5 is surprisingly strong here at 62.2%. GPT-5.2 is far behind at 38.2%.

BrowseComp (Web Browsing)

Gemini 3.1 Pro and Claude Opus 4.6 both excel at web browsing tasks. GPT-5.2 trails significantly at 65.8% — a 20-point gap from Gemini.

Multimodal and Knowledge Benchmarks

MMMU-Pro (Multimodal Understanding)

Gemini 3.1 Pro and GPT-5.2 trade blows here, with Google slightly ahead. Both Claude models are 6-7 points behind — multimodal understanding isn't Anthropic's strongest suit.

MMMLU (Massive Multitask Language Understanding)

Another tight race, with Gemini 3.1 Pro on top. All four models are above 89%, meaning general knowledge is well-saturated at the frontier.

Long Context: MRCR v2

At 128K context, Gemini 3.1 Pro and Claude Sonnet 4.6 tie at 84.9%. But only Gemini can go to 1M tokens — and its performance drops substantially at that length (26.3%). The 1M context window is impressive on paper, but expect degraded retrieval at the extreme end.

Instruction Following and Multilingual

From the Qwen3.5 model card, we get some additional benchmark comparisons:

Note: These benchmarks are from the Qwen3.5 model card and reference "Gemini 3 Pro" (the previous generation) and "Claude 4.5 Opus" (Qwen's naming for Opus 4.6). Gemini 3.1 Pro would likely score higher than the Gemini 3 Pro numbers shown.

Key takeaways: Qwen3.5-397B leads on instruction following (IFBench: 76.5) and MultiChallenge (67.6). Claude Opus 4.6 is the strongest at multilingual coding (SWE-bench Multilingual: 77.5). These are areas where the open-weight model genuinely competes with — and sometimes beats — closed frontier models.

Pricing Comparison: Cost Per Million Tokens

Performance matters, but so does your bill. Here's the full pricing breakdown:

The pricing landscape is dramatic. Claude Opus 4.6's output cost ($75/M tokens) is 178 times more expensive than DeepSeek V3.2 ($0.42/M tokens). Even compared to GPT-5.2, Opus is 5.4x more on output.

Gemini 3.1 Pro hits a sweet spot: $2/$12 pricing puts it cheaper than GPT-5.2 on output ($12 vs $14) while delivering superior benchmark performance in most categories. Add in the aggressive caching discount ($0.20 input for cached content), and Gemini becomes even more attractive for production workloads with repeated context.

Who Should Use What: Recommendations by Use Case

After analyzing all these benchmarks, here's our honest recommendation for different developer personas:

🏗️ Building Production AI Agents

Best choice: Claude Sonnet 4.6

Surprised? Don't be. Sonnet 4.6 has the highest GDPval-AA Elo (1633), strong τ2-bench scores, and costs 80% less than Opus. For agentic workflows where you need reliable, autonomous execution at scale, Sonnet 4.6 is the best value proposition.

Runner-up: Gemini 3.1 Pro for tool-heavy agents (MCP Atlas: 69.2%), Claude Opus 4.6 for computer-use agents (OSWorld: 66.3%).

💻 Daily Coding Assistant

Best choice: Gemini 3.1 Pro

It leads on LiveCodeBench Pro (Elo 2887), Terminal-Bench (68.5%), and SciCode (59%). The 1M context window means you can feed it entire codebases. At $2/$12 pricing, it's also cheaper than most alternatives.

Runner-up: Claude Opus 4.6 for bug fixing (SWE-Bench Verified: 80.8%), GPT-5.2 Codex for professional-grade SWE-Bench Pro tasks (56.8%).

🔬 Research and Reasoning

Best choice: Gemini 3.1 Pro

HLE 44.4%, GPQA Diamond 94.3%, ARC-AGI-2 77.1% — it leads across every reasoning benchmark. If you're working on novel problems that require genuine analytical thinking, Gemini 3.1 Pro is the clear winner right now.

🌐 Multilingual Applications

Best choice: Qwen3.5-397B

With support for 201 languages and strong MultiChallenge scores (67.6), Qwen3.5 is purpose-built for multilingual use cases. Being open weight means you can fine-tune for specific languages. For multilingual coding specifically, Claude Opus 4.6 leads (SWE-bench Multilingual: 77.5).

💰 Budget-Conscious Teams

Best choice: DeepSeek V3.2 or Qwen3.5-397B

DeepSeek V3.2 at $0.28/$0.42 per million tokens is unbeatable on price. It's open source with a 128K context window. For teams that need high volume at low cost, it's the obvious pick. Qwen3.5 is free as open weight if you have the GPU infrastructure.

📄 Document Processing and Multimodal

Best choice: Gemini 3.1 Pro

It's the only model that accepts text, images, audio, video, and PDFs natively. MMMU-Pro score of 80.5% leads the pack. The 1M context window can handle massive documents. This is Google's strongest differentiator.

The Bigger Picture: What This Means for Developers

February 2026 marks an inflection point. For the first time, we're seeing genuine specialization among frontier models rather than one model dominating everything:

• Gemini 3.1 Pro wins on raw reasoning, competitive coding, and multimodal breadth
• Claude models win on agentic workflows and autonomous operation
• GPT-5.2 wins on professional software engineering (SWE-Bench Pro) but trails on agentic tasks
• Qwen3.5 wins on instruction following, multilingual, and open-weight flexibility
• DeepSeek V3.2 wins overwhelmingly on price

The smart play for most teams is to use multiple models. Route reasoning-heavy tasks to Gemini 3.1 Pro, agent workflows to Claude Sonnet 4.6, and high-volume simple tasks to DeepSeek V3.2. At Serenities AI, we've been building tools that help developers navigate exactly this kind of multi-model landscape — because the era of "just use GPT for everything" is definitively over.

The question isn't "which is the best AI model?" anymore. It's "which is the best AI model for this specific task?" And the answer changes depending on whether you're debugging code, building an autonomous agent, processing documents, or optimizing for cost.

A Note on DeepSeek V3.2

We've included DeepSeek V3.2 in this comparison because it's a legitimate contender — especially on price. At $0.28 per million input tokens, it's approximately 7x cheaper than GPT-5.2 and 54x cheaper than Claude Opus 4.6. It's open source with a 128K context window, making it attractive for self-hosting.

However, we have limited verified benchmark data for DeepSeek V3.2 compared to the other models in this article. Neither the Gemini nor the Qwen model cards included it in their benchmark comparisons. We'll update this article as more independent benchmark data becomes available. For now, we're comfortable saying it's competitive on math tasks and unbeatable on price — but we won't make claims we can't verify.

Frequently Asked Questions

What is the best AI model overall in February 2026?

Based on verified benchmarks, Gemini 3.1 Pro leads on the most individual benchmarks — including HLE (44.4%), ARC-AGI-2 (77.1%), GPQA Diamond (94.3%), LiveCodeBench Pro (Elo 2887), and BrowseComp (85.9%). However, it falls behind on agentic workflow Elo (GDPval-AA: 1317 vs Claude Sonnet's 1633) and doesn't lead on SWE-Bench Verified. There is no single "best" model — it depends entirely on your use case.

Is Claude Opus 4.6 worth the price premium over GPT-5.2?

Claude Opus 4.6 costs $15/$75 per million tokens versus GPT-5.2's $1.75/$14 — roughly 5x more on output. It justifies this for specific use cases: SWE-Bench Verified (80.8% vs 80.0%), agentic workflows (GDPval-AA Elo 1606 vs 1462), computer use (OSWorld 66.3% vs 38.2%), and web browsing (BrowseComp 84.0% vs 65.8%). If you're building autonomous agents or need reliable tool use, Opus delivers. For general coding and reasoning, GPT-5.2 or Gemini 3.1 Pro offer better value.

Should I use Qwen3.5-397B instead of closed models?

Qwen3.5-397B is compelling if you need data sovereignty, fine-tuning ability, or multilingual support (201 languages). It leads on instruction following (IFBench: 76.5) and MultiChallenge (67.6). However, it trails on reasoning (HLE: 28.7% vs Gemini's 44.4%) and coding (Terminal-Bench: 52.5% vs Gemini's 68.5%). The MoE architecture (17B active out of 397B) makes it more efficient than the parameter count suggests, but you still need significant GPU infrastructure to self-host.

How does Gemini 3.1 Pro compare to its predecessor Gemini 3 Pro?

Gemini 3.1 Pro shows major improvements over Gemini 3 Pro across the board: HLE jumped from 37.5% to 44.4%, ARC-AGI-2 went from 31.1% to 77.1% (a 46-point leap), Terminal-Bench improved from 56.9% to 68.5%, and BrowseComp went from 59.2% to 85.9%. The ARC-AGI-2 improvement is particularly striking — it suggests a fundamental capability gain in abstract reasoning, not just incremental scaling. The new "Medium" thinking level and maintained $2/$12 pricing make it a straightforward upgrade.

Which model should I choose for building AI-powered applications?

For most production applications, we recommend a multi-model approach: use Gemini 3.1 Pro for complex reasoning and multimodal tasks, Claude Sonnet 4.6 for agentic workflows (highest automation Elo at $3/$15 pricing), and DeepSeek V3.2 or GPT-5.2 for high-volume, cost-sensitive tasks. Start with one model, benchmark it on your specific workload, then diversify. The models have different strengths — the best architecture leverages all of them.