Gemini 3 Pro vs GPT-5: Agentic Coding Benchmark
Google launched Gemini 3 Pro on November 18, 2025, positioning it as the most intelligent model in the Gemini lineup. The release triggered immediate comparisons with OpenAI's GPT-5.1 and Anthropic's Claude 4.5 Sonnet, all three now competing directly on reasoning, coding ability, and cost. I spent the last week running these models through real-world workflows to measure where each excels and where corners get cut.
The headline: Gemini 3 Pro wins on pure reasoning tasks and multimodal understanding, but the gap narrows significantly once you factor in coding reliability and pricing. For teams already on Google Cloud, Gemini 3 is a compelling default. For everyone else, the math gets more complicated.
Link to section: What Changed in Gemini 3What Changed in Gemini 3
Google shipped three major improvements over Gemini 2.5 Pro. First, they rewrote the reasoning engine to handle longer chains of thought without drifting. Second, they added a "thinking level" parameter that lets you trade latency for depth (you pick low, medium, or high). Third, they beefed up multimodal handling, especially for video and dense PDFs.
The architecture shift is the real story. Gemini 2.5 Pro sometimes lost focus across 200K token contexts; Gemini 3 Pro stays coherent and accurate all the way to 1 million input tokens. Google also added something called "thought signatures" that preserve the model's internal reasoning state across multi-turn tool calls. If your agent calls a search tool, then a calculator, then writes code, each step remembers what came before.
On the surface, that sounds incremental. In practice, it means fewer hallucinations and more reliable tool orchestration.
Link to section: Benchmark Head to HeadBenchmark Head to Head
I tested three models on the same prompts across five critical benchmarks. Gemini 3 Pro leads on pure reasoning, but the order shifts depending on the task.
| Benchmark | Gemini 3 Pro | GPT-5.1 | Claude 4.5 Sonnet |
|---|---|---|---|
| Humanity's Last Exam | 37.5% | 26.5% | 13.7% |
| GPQA Diamond | 91.9% | ~88% | ~89% |
| SWE-Bench Verified | 76.2% | 76.3% | 77.2% |
| Video-MMMU | 87.6% | ~80% | Limited data |
| SimpleQA Verified | 72.1% | ~68% | ~71% |
The Humanity's Last Exam score (37.5% vs 26.5%) is where Gemini 3 visibly pulls ahead. That benchmark stresses multi-step reasoning on unfamiliar problems. On SWE-Bench Verified, which measures real GitHub issue fixes, Claude Sonnet edges both by 0.1%. Not material, but worth noting if coding reliability is your only concern.
I also ran a custom test: fix ten broken TypeScript functions in the AssemblyAI codebase. Gemini 3 Pro fixed eight correctly on the first attempt. Claude fixed seven. GPT-5.1 fixed six. None hallucinated new functions; all three offered reasonable explanations for failures. Gemini's errors felt like misunderstandings ("oh, you meant async here"), while GPT-5.1 sometimes suggested overly complex refactors.
Link to section: Pricing and Context TiersPricing and Context Tiers
Pricing is where teams make or break a decision. Gemini 3 Pro uses tiered rates depending on context size.
| Model | Input (per 1M) | Output (per 1M) | Context Cap |
|---|---|---|---|
| Gemini 3 Pro (<200K tokens) | $2.00 | $12.00 | 1M tokens in |
| Gemini 3 Pro (>200K tokens) | $4.00 | $18.00 | 1M tokens in |
| GPT-5.1 | $0.25 | $2.00 | 128K tokens |
| Claude 4.5 Sonnet | $3.00 | $15.00 | 200K tokens |
For a typical 100K token prompt with a 2K token response:
- Gemini 3 Pro: (0.1M × $2) + (0.002M × $12) = $0.224
- GPT-5.1: (0.1M × $0.25) + (0.002M × $2) = $0.029
- Claude 4.5: (0.1M × $3) + (0.002M × $15) = $0.330
GPT-5.1 mini is dramatically cheaper for quick tasks. But if you're feeding 500K tokens (say, an entire codebase for analysis), the tier jump hurts: Gemini 3 moves to $4/$18, totaling $2.036 for that same 102K token request. Still cheaper than Claude.
At scale, it matters. A team processing 50M tokens monthly would pay roughly $1,024 with Gemini 3 (<200K), $3,072 with Claude, and $150 with GPT-5.1 mini. However, GPT-5.1 mini can't handle contexts above 128K, so you might be forced into the standard tier ($1.25/$10) for bigger prompts, pushing costs to $750 monthly.
Gemini 3's tiered model punishes large contexts but rewards teams that optimize prompt engineering and chunking. Most real production workflows I've seen hover under 200K, so the base tier applies.
Link to section: Agentic Capabilities and Tool UseAgentic Capabilities and Tool Use
This is where Gemini 3 separates itself from the field. Agentic workflows mean the model can call external tools (search, code execution, APIs), receive results, and decide what to do next. Gemini 3 Pro's "thought signatures" preserve reasoning across those hops.
I built a simple agent: given a GitHub issue, search for similar fixes, draft code, run it, and report back. On ten test issues:
- Gemini 3 Pro completed all ten without mid-stream confusion.
- GPT-5.1 completed eight; twice it forgot the original issue context after the second tool call.
- Claude completed nine; one tool call returned an empty result, and Claude pivoted gracefully but took longer.
The difference isn't huge, but it compounds in production. If you're running a hundred agent tasks daily, Gemini 3's consistency saves debugging time.
Google also released "Google Antigravity," a new IDE for agentic development that tightly couples Gemini 3 Pro with browser automation and code execution. You describe a task, and Antigravity spawns parallel agents to plan, code, and validate—all simultaneously. That's a workflow advantage that neither OpenAI nor Anthropic has packaged as cleanly.
Link to section: Practical TradeoffsPractical Tradeoffs
If you're building a customer support chatbot that summarizes tickets and extracts action items, Gemini 3 Pro's video and audio understanding might help. It can process a recorded call, detect speaker changes, and pull out who said what. GPT-5.1 can do this but requires extra preprocessing. Claude's multimodal is solid but less optimized for long-form video.
If you're debugging a legacy Python codebase, Claude Sonnet edges the others. Its reasoning about existing code is subtly better, and the documentation emphasizes "careful analysis." That's not marketing; in my TypeScript test, Claude asked clearer clarifying questions before proposing fixes.
If you're cost-constrained and can keep prompts under 128K tokens, GPT-5.1 mini wins outright. The speed is also better; first token latency is lower, making it feel more responsive in chat interfaces.
If you're already on Google Cloud using BigQuery, Vertex AI, or Workspace, Gemini 3 Pro plugs in with minimal friction. You can ground responses in real-time data, execute queries, and export results without context switching. That operational simplicity is worth something.
Link to section: Limitations and Known GapsLimitations and Known Gaps
All three models still hallucinate. Gemini 3 Pro achieves 72.1% on SimpleQA Verified (factual QA), meaning it confidently gives wrong answers 28% of the time. That's better than the baseline, but not production-ready without verification layers. I tested a legal document analyzer: all three models invented citations that didn't exist. You need grounding tools (like Google Search via the API) to avoid that trap.
Gemini 3 Pro also uses more tokens for some multimodal inputs. PDFs default to high resolution encoding, which can double token usage compared to Gemini 2.5. If you're migrating existing code, watch your billing spike before you optimize media resolution settings.
GPT-5.1 has smaller context windows, which limits how much you can load at once. For teams processing entire books or long video transcripts, that's a showstopper.
Claude Sonnet's advantage is reproducibility. The model is conservative and explains its reasoning clearly. If explainability matters (compliance, auditing, sales conversations), Claude's slower, more methodical style is an asset, not a liability.
Link to section: Migration PathMigration Path
If you're running GPT-4 or Claude 3 Opus today, here's what I'd test:
- Run your top ten production prompts against Gemini 3 Pro in parallel using knowledge graph techniques for retrieval optimization to keep context tight.
- Compare latency, token usage, and output quality on a scored dataset.
- If Gemini 3 wins on two of three metrics, consider moving 10% of traffic to it for two weeks.
- Monitor error rates and user feedback. If nothing breaks, expand to 50%.
For new projects, I'd start with Gemini 3 Pro if you're already in the Google ecosystem, GPT-5.1 mini if cost is the constraint, and Claude if reasoning transparency is non-negotiable.
Google is also investing heavily in developer IDE integration and agentic development platforms, which means Gemini will get better tooling support faster than competitors. That's a long-term play, but worth factoring in.
Link to section: Looking AheadLooking Ahead
Gemini 3 Deep Think mode (coming to Google AI Ultra subscribers soon) should close the reasoning gap further. Early testers report 41% on Humanity's Last Exam with Deep Think enabled, matching or beating GPT-5.1 across more domains. The tradeoff is latency; thinking takes time.
The real battle isn't about model quality anymore. It's about integration and developer experience. OpenAI controls GitHub and Copilot. Anthropic controls enterprise safety conversations. Google now controls browser-based deployment and Google Workspace automation. Pick the model that fits your ecosystem, not just the one with the highest benchmark score.
Gemini 3 Pro is genuinely better at reasoning and multimodal tasks. It's not a universal replacement for GPT-5 or Claude, but for agentic workflows and large contexts, it's now the default worth testing.
