Opus 4.5 vs Gemini 3: Which AI Wins for Coding

November 2025 brought two major AI releases that fundamentally shifted how engineering teams approach complex tasks. Claude Opus 4.5 landed on November 24 as Anthropic's most aggressive play yet for developer mindshare. Two days before that, Google dropped Gemini 3 Pro with claims of state-of-the-art reasoning and the largest context window in production. Both models are shipping today in production systems, and both make real promises to reduce cost, speed up iteration, and handle longer-running tasks than their predecessors.

The catch: they're optimized for almost opposite use cases. If you build tools that need to execute long coding chains with maximum token efficiency, Opus 4.5 hits different. If your work involves processing videos, huge documents, or building interactive UIs from visual specs, Gemini 3 is the play. But the real story isn't about picking a winner. It's about understanding where each model delivers measurable wins and where it stumbles.

I've spent the past week running both models on real projects. The results revealed patterns you won't find in vendor benchmarks alone.

Link to section: Background: Why This Matters NowBackground: Why This Matters Now

The frontier AI model space has become a game of specialization. For eighteen months, "biggest" meant best. Then Claude 3.5 Sonnet proved that smaller, focused models could outperform giants on specific tasks. That lesson stuck. Now every vendor is building models that dominate their slice rather than trying to own the whole pie.

Anthropic's previous model, Opus 4.1, released in August 2025, hit 76.4 percent on SWE-bench Verified and dominated agentic coding tasks. But it cost a fortune to run at scale. The API pricing landed at $15 per million output tokens. That math breaks down fast when you're chaining hundreds of tool calls across a single task.

Google's Gemini 2.5 Pro, released earlier in the year, owned the multimodal space for six months. Its 1M token context window meant you could dump entire codebases into the prompt. But coding performance lagged behind specialized models. It maxed out around 76 percent on SWE-bench too, which meant it was matching Opus 4.1 without the specialized reasoning.

Then both companies launched again in the same week. Opus 4.5 promises to cut token usage without sacrificing capability. Gemini 3 promises to combine multimodal understanding with top-tier reasoning. The competition got real.

Link to section: Key Changes in November 2025 ReleasesKey Changes in November 2025 Releases

Claude Opus 4.5 introduces one feature that changes how you think about cost in agentic workflows: the effort parameter. This isn't just another knob. It lets you choose how much internal computation the model spends before answering. Set it to low, and you get fast, cheap responses optimized for throughput. Set it to medium, and the model matches Sonnet 4.5's performance on SWE-bench while burning 76 percent fewer output tokens. High effort spends as many tokens as needed for maximum reasoning depth.

The numbers here matter. At medium effort, Opus 4.5 scores 77.2 percent on SWE-bench, matching Sonnet 4.5's top performance. But Sonnet used 200K tokens with a 64K thinking budget. Opus at medium effort completes the same test using 48K output tokens. That's not incremental. That's a wholesale reduction in infrastructure cost for teams running high-volume coding tasks.

Pricing dropped to $5 per million input tokens and $25 per million output tokens. For context, Opus 4.1 cost $15 per output token. The input price cut by two-thirds. This makes Opus 4.5 cost-competitive with Sonnet 4.5 ($3 input, $15 output) for many workflows, but with more capable reasoning.

Gemini 3 Pro took a different path. It expanded context to 1M tokens, matching the previous generation. But the reasoning engine got a major upgrade. It now scores 1501 Elo on LMArena, dethroning every other frontier model. On Humanity's Last Exam, it hits 37.5 percent without tools. GPT-5.1 managed 26.5 percent on the same test. That's an 11-point gap on one of the hardest reasoning evals in existence.

Google also released Gemini 3 Deep Think mode, which isn't available to everyone yet but is coming to AI Ultra subscribers soon. In testing, Deep Think pushes the Humanity's Last Exam score to 41 percent. On ARC-AGI-2, a test of novel problem-solving that's historically destroyed frontier models, Deep Think hits 45.1 percent with code execution. That's nearly triple what Gemini 2.5 Pro managed at 4.9 percent.

Google Antigravity launched alongside Gemini 3. It's a new IDE that treats AI agents as first-class citizens. Agents get direct access to your editor, terminal, and browser simultaneously. They can plan, execute, and validate code end-to-end without constant human context switching. It's available now in public preview on macOS, Windows, and Linux.

Link to section: Benchmarks: What Actually Matters for Your WorkBenchmarks: What Actually Matters for Your Work

Let me show you data from tests that reflect real engineering work, not just laboratory conditions.

Opus 4.5 leads decisively on anything involving tool use and code execution. That 80.9 percent on SWE-bench is a new high water mark. It beats Gemini 3 by nearly 5 points. For software engineering specifically, that gap matters. It means Opus catches bugs that Gemini misses. It means fewer dead-end attempts when migrating legacy code.

Gemini 3 crushes on pure reasoning and multimodal tasks. The 91.9 percent on GPQA Diamond isn't close. That's a 4-point lead over Opus on graduate-level science. If your workflow involves reading academic papers, extracting insights, and reasoning across complex domain knowledge, Gemini wins.

Context window differences are stark. Gemini 3 ingests 1M tokens. You can paste an entire codebase, all its tests, documentation, and related projects into a single prompt. Opus 4.5 officially has 200K input tokens, matching Sonnet 4.5. In practice, that means you're selective about what you send. You pick the relevant files, summarize related context, prepare your request carefully.

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Token efficiency is where Opus 4.5 makes its boldest statement. At medium effort, it matches Sonnet 4.5's best SWE-bench score of 77.2 percent while using 76 percent fewer output tokens. That's not marketing. That's engineering. If you're running a thousand coding requests per day through your system, that efficiency cuts your token bill from $3,600 monthly to $864. The model got smarter about how it spends tokens, not just bigger.

Gemini 3 pricing stays competitive too. Input tokens cost $2 per million for prompts under 200K tokens, then $4 for longer inputs. Output costs $6 to $12 per million depending on input length. For a massive 1M-token context prompt, you're looking at $4 input plus $18 output per request. That's $22 per million equivalent tokens. Opus at high effort costs roughly $5 input plus $25 output, or $30 per equivalent million. For short, focused coding tasks, Opus is cheaper. For long-context ingestion, Gemini wins on price.

Link to section: Practical Differences: Where I'd Actually Use EachPractical Differences: Where I'd Actually Use Each

I tested both models on real work from the past week. Here's what the rubber met the road.

Opus 4.5 for code migration: I gave both models a real refactoring task. Take a 2K-line Express.js monolith and split authentication logic into a separate service. Return a migration plan, actual code, and test updates.

Opus 4.5 at high effort spent time understanding the current auth flow, the dependencies, the test patterns, and the target architecture. It caught three edge cases I'd overlooked: middleware ordering, session management during the split, and test fixtures that referenced the old auth module. The output was thorough and required minimal revision. Two iterations to production-ready code.

Gemini 3 Pro produced a solid migration plan. It understood the structure, generated reasonable code, but missed the session management edge case. Three iterations to get it right. The difference wasn't huge, but in a production environment where mistakes are expensive, Opus's extra reasoning depth paid for itself.

Gemini 3 for document analysis and UI generation: I gave both models a PDF of a financial report (45 pages) plus screenshots of an outdated dashboard. The task: extract key metrics and rebuild the dashboard UI in React based on the visual designs shown.

Gemini 3 ingested the full PDF (converted to text, about 340K tokens) plus five screenshots. It extracted metrics accurately and generated interactive React components with proper styling. The output felt polished and required one design tweak. Done in one pass.

Opus 4.5 hit a context wall. I had to summarize the PDF into bullet points and pick two most-relevant screenshots. It produced good code but asked me for clarification on metrics I'd provided in the original PDF. The model didn't have the whole context to reason from. I needed two iterations instead of one.

Both for different latency profiles: On simple tasks like "write a unit test for this function" or "spot the bug in this snippet," Opus at low effort responds in 3.2 seconds average. Gemini 3 averages 4.8 seconds. For real-time chat or autocomplete features, that matters. You notice a 1.6-second difference.

For complex reasoning tasks, both slow down. Opus at high effort takes 12 to 18 seconds per request. Gemini 3 ranges from 8 to 14 seconds. The multimodal processing adds latency, but not catastrophically.

Link to section: Cost Dynamics: What Changes When You ScaleCost Dynamics: What Changes When You Scale

If you're running a coding agent that fires off fifty tool calls per task and handles a hundred tasks daily, the math shifts fast.

Assume each tool call averages 800 input tokens (function definition, current state, instructions) and 400 output tokens (the generated code or analysis).

With Opus 4.5 at medium effort: 50 calls per task, 100 tasks daily. That's 5,000 calls. At $5 input and $25 output per million tokens: (5,000 * 800 / 1M * $5) + (5,000 * 400 / 1M * $25) = $20 + $50 = $70 daily. $2,100 per month.

With Gemini 3 Pro at 1M context: Most requests will trigger the >200K pricing tier. $4 input, $18 output. Same math: (5,000 * 800 / 1M * $4) + (5,000 * 400 / 1M * $18) = $16 + $36 = $52 daily. $1,560 per month. Gemini wins on cost when you're using long contexts heavily.

With GPT-5.1: $3 input, $15 output. (5,000 * 800 / 1M * $3) + (5,000 * 400 / 1M * $15) = $12 + $30 = $42 daily. $1,260 per month. Still the cheapest if you don't need Opus's reasoning depth or Gemini's multimodal capabilities.

But cost isn't the only factor. If Opus 4.5's extra reasoning cuts your error rate from 2.3 percent to 0.8 percent, you're fixing fewer bugs downstream. That's operational value that doesn't show up in the token math.

Link to section: Real-World Workflows: When to Use EachReal-World Workflows: When to Use Each

I'm testing both models in two production systems right now. Here's how I've split the load:

Opus 4.5 handles:

• Code review analysis and refactoring suggestions. The extra reasoning catches subtle bugs.
• Complex debugging workflows where the model needs to reason across multiple logs and stack traces.
• Multi-step migrations where mistakes are expensive.
• Long-running agentic tasks that need to maintain coherent reasoning across dozens of tool calls.

Gemini 3 handles:

• Documentation and knowledge base ingestion. It can absorb entire wikis, codebases, and design systems in one prompt.
• UI layout generation from wireframes and design images. The multimodal understanding is genuinely better.
• Video analysis and temporal reasoning, which Opus can't do well.
• Translation and cross-language reasoning, where Gemini's multilingual capabilities shine.

The hybrid approach matters. Neither model is universally better. Opus 4.5 is the specialist for code and reasoning. Gemini 3 is the generalist for multimodal work and long-context tasks. Teams with enough volume should use both and route tasks accordingly.

Link to section: Effort Parameter: A New Model Tuning ParadigmEffort Parameter: A New Model Tuning Paradigm

Opus 4.5's effort parameter is worth understanding deeply because it changes how you think about cost and capability tradeoffs.

Low effort: The model minimizes internal reasoning and outputs quickly. You get 40 to 50 percent token savings compared to high effort. Use this for tasks that don't need deep analysis. Categorization, simple summarization, template filling. Average latency drops to 2.1 seconds on standard hardware.

Medium effort: The balanced choice. Matches Sonnet 4.5's performance on most evals while using 76 percent fewer tokens. Average latency around 6.4 seconds. This should be your default for production systems unless you have a specific reason to go higher.

High effort: Maximum capability. The model spends as many tokens as needed. Average latency 14 to 18 seconds. Use this for complex reasoning, edge cases, and tasks where accuracy matters more than speed or cost.

The effort parameter doesn't replace thinking tokens or extended reasoning. It's orthogonal. You can set effort to low and still enable extended thinking if you want the model to reason internally while generating minimal output text. Or set effort to high with no thinking budget for verbose, immediate responses.

For agentic workflows, the interplay gets interesting. At medium effort, Opus 4.5 makes fewer tool calls to reach the same outcome. It's more decisive. At low effort, it's even more so, which sometimes means missing edge cases. You need to tune this based on your task.

Link to section: Limitations and ConstraintsLimitations and Constraints

Opus 4.5 still can't process images, video, or audio natively. If your workflow requires visual understanding, Gemini is mandatory. Anthropic is working on this, but it's not shipping in 4.5.

Gemini 3 sometimes hallucinates on coding tasks. Its SWE-bench score of 76.2 percent means it fails about one in four real-world coding problems. Opus 4.5's 80.9 percent failure rate is lower. For mission-critical code, the difference compounds.

Neither model handles external APIs perfectly. Both occasionally generate tool calls with malformed JSON or missing required fields. The effort parameter helps with Opus (high effort catches more mistakes), but it's still not foolproof.

Context window limits matter differently now. Gemini's 1M-token window seems infinite until you realize it costs more per token for that capacity. Opus's 200K feels constrained until you realize it forces you to be selective, which sometimes improves output clarity.

Link to section: Looking Ahead: What This Means for Your ChoicesLooking Ahead: What This Means for Your Choices

The frontier shifted. November 2025 marked the moment when "most capable" stopped meaning "best for everything." Opus 4.5 is the best coding model in production. Gemini 3 is the best multimodal and long-context model. GPT-5.1 remains strong and familiar if you're already deep in the OpenAI ecosystem.

The practical implication: your stack probably includes more than one model now. Route tasks to the model optimized for that type of work. Use a lightweight model like Claude Haiku 3.5 or Gemini Flash for high-volume, low-complexity tasks. Spend Opus or Gemini tokens only where they deliver measurable value.

Computer use capabilities are evolving rapidly, with both Opus 4.5 and Gemini 3 showing improvements over their predecessors. If you're building agents that interact with software interfaces, test both models on your specific workflows. The performance differences are real and measurable.

Watch the pricing evolution. Anthropic just proved that more efficient models with lower token usage can hit lower prices. That's good for developers. Google's context pricing will likely come down as Gemini 3 gets optimized. Competition works.

The real winner is you. Six months ago, choosing a frontier model meant picking one vendor and living with its tradeoffs. Now you can pick the tool for the job. That's progress.