Claude Opus 4.5 vs Gemini 3 Pro: Frontier Reasoning Model Head-to-Head
Two flagship reasoning models launched a week apart. We put Anthropic's Opus 4.5 and Google's Gemini 3 Pro through the same coding, reasoning, long-context, and long-horizon agent benchmarks and scored the rounds on published results.
Opus 4.5 takes the overall by two points on the strength of real-world software engineering (SWE-bench Verified, Terminal-Bench) and multi-file agent coding, and it does it at a lower total per-task cost than the headline pricing suggests because it emits far fewer output tokens. Gemini 3 Pro takes abstract reasoning on ARC-AGI-2 wide open (with search enabled), leads long-horizon planning on Vending-Bench 2, and dominates competitive coding and math. For coding agents and multi-file engineering, Opus 4.5 is the higher-scoring default. For research, long-horizon planning, and cost-sensitive high-volume workloads on prompts under 200K, Gemini 3 Pro is the defensible pick.
Claude Opus 4.5 and Gemini 3 Pro shipped six days apart in November 2025 and are the two flagship reasoning models the market has settled on going into H2 2026. Both list a 1M-token context window, both expose an explicit reasoning-effort control, and both are pitched at the same buyers: coding agents, research workloads, and long-horizon autonomous tasks. The buying decision reduces to which one wins on the work a team actually does.
Every round below names the concrete procedure behind it. Coding and reasoning rounds are scored on published third-party benchmark results with a known answer key. Cost and latency rounds are pure measurement against each vendor's rate card. Context and long-horizon rounds are scored on the standard tests each vendor and independent evaluators have already run.
| Test category | Winner | Result & method |
|---|---|---|
| Real-world software engineering | Claude Opus 4.5 | Opus 4.5 posted 80.9% on SWE-bench Verified, ahead of Gemini 3 Pro at 76.2% on the same benchmark. Anthropic also notes Opus 4.5 is the first model to cross 80% on this suite. The gap is 4.7 percentage points on a benchmark where a point is meaningful, and it holds across independent evaluations. How we measured it: SWE-bench Verified pass rate, a human-curated subset of 500 GitHub issues where the model has to modify the correct files in a Python repository to make the existing test suite pass. Scored single-attempt, no tools beyond the standard agent scaffold. |
| Terminal / command-line agent tasks | Claude Opus 4.5 | Opus 4.5 scored 59.3% on Terminal-Bench, ahead of Gemini 3 Pro's 54.2%. This is the round most tied to real coding-agent workflows: multi-step file editing, running the repo's own tests, and recovering from failed commands. The 5.1-point margin is one of the largest in the coding suite. How we measured it: Terminal-Bench 2.0, in which models drive a terminal to edit files, run commands, and debug across tasks drawn from real agent frameworks. Scored on task success rate under the Terminus-2 agent. |
| Competitive coding | Gemini 3 Pro | Gemini 3 Pro posted an Elo of 2,439 on LiveCodeBench Pro, roughly 200 points ahead of GPT-5.1 (2,243). For teams whose work is dominated by novel algorithm design or contest-style problems rather than patching existing codebases, Gemini 3 Pro is the stronger pick. How we measured it: LiveCodeBench Pro Elo, an ELO-ranked competitive-programming benchmark that tests novel algorithmic problem generation rather than repository patching. |
| Abstract reasoning | Claude Opus 4.5 | Opus 4.5 hit 37.6% on ARC-AGI-2, more than double GPT-5.1 (17.6%) and ahead of Gemini 3 Pro's 31.1% on the same public numbers. Gemini 3.1 Pro subsequently jumped to 77.1% in its February 2026 preview, but the Gemini 3 Pro GA number is what a team can build on today in Vertex AI without accepting preview-tier terms. How we measured it: ARC-AGI-2, novel visual-logic puzzles solved from just a few examples, designed to resist memorization. Scored on the ARC Prize Verified track without external tools. |
| Long-horizon agent planning | Gemini 3 Pro | Gemini 3 Pro finished with a mean net worth of $5,478.16, ahead of Opus 4.5 at $4,967.06. Both are strong absolute results (Opus 4.5's total is a 23% improvement over Sonnet 4.5), but on this specific test of coherent long-horizon strategy, Gemini 3 Pro takes the round. How we measured it: Vending-Bench 2, in which the model runs a simulated vending-machine business for a full year, making thousands of coherent decisions to maximize profit. Scored on final net worth (mean). |
| Expert-level knowledge | Gemini 3 Pro | On the no-tools track, Gemini 3 Pro scored 37.5% versus Opus 4.5's 87.0%, but the Opus number is measured on the separate MMLU / knowledge track, so this round scores the head-to-head as published. Gemini 3 Pro leads HLE 37.5% vs Opus 4.5's ~43.2% with search enabled, roughly state of the art and within noise. On PhD-level science (GPQA Diamond), Gemini 3 Pro posts 91.9% versus Opus 4.5's 87.0%, and that 4.9-point margin is what decides the round. How we measured it: Humanity's Last Exam, 2,500 subject-diverse expert questions covering mathematics, humanities, and natural sciences from nearly 1,000 expert contributors. Reported both without tools and with web search / code execution. |
| List price per million tokens | Gemini 3 Pro | Gemini 3 Pro lists at $2 input / $12 output per million tokens for prompts under 200K, rising to $4/$18 above 200K. Opus 4.5 lists at $5 input / $25 output. On raw sticker price, Gemini 3 Pro is 2.5x cheaper on input and 2.1x cheaper on output. Prompt caching (Anthropic: cache hits at 0.1x base input) and the 50% Batch API discount narrow the gap for cache-heavy workloads, but at list price this round isn't close. How we measured it: Anthropic and Google's published API rate cards as of August 6, 2026, compared at standard tier for prompts under 200K tokens. |
| Cost per completed task | Claude Opus 4.5 | The list-price gap partially reverses on total per-task cost because Opus 4.5 is materially less verbose. Anthropic reports Opus 4.5 matches Sonnet 4.5's best SWE-bench score using 76% fewer output tokens at medium effort, and exceeds Sonnet 4.5 by 4.3 points at high effort while still using 48% fewer tokens. GitHub confirmed Opus 4.5 cuts token usage roughly in half against its internal coding benchmarks. For output-heavy coding-agent workloads the effective cost-per-task gap is much smaller than the 2.5x sticker gap suggests, and can flip depending on verbosity. How we measured it: Measured against Anthropic's published claim that Opus 4.5 produces same-quality work using roughly 76% fewer output tokens than Sonnet 4.5, then priced against each vendor's rate card on a workload dominated by output tokens. |
| Long-context recall | Gemini 3 Pro | Gemini 3 Pro scored 77.0% on MRCR v2 at 128K, and Google reports it extends its lead over Gemini 2.5 Pro by 9.9 points at the 1M mark. Both models advertise a 1M-token window, but published needle-in-a-haystack numbers currently favor Gemini 3 Pro. Anthropic exposes Opus 4.5's 1M window via a beta header, and its published long-context recall data is thinner. How we measured it: MRCR v2, a needle-in-a-haystack retrieval test measuring whether a model can accurately recall specific information from a large context window, scored at the 128K average length. |
Opus 4.5 and Gemini 3 Pro landed six days apart in November 2025 and are now the two flagship reasoning models most enterprise buyers are choosing between. Both advertise a 1M-token context window, both expose an explicit reasoning-effort control, and both are pitched at the same coding, research, and long-horizon agent workloads. The choice reduces to which one wins the rounds a given team actually runs.
Reading the result
The overall margin is two points, narrow enough that the round breakdown matters more than the headline. Opus 4.5 took five of nine rounds: SWE-bench, Terminal-Bench, ARC-AGI-2 (on public GA numbers), and both cost-per-task and its share of the coding suite. Gemini 3 Pro took four: LiveCodeBench Pro, Vending-Bench 2, long-context recall, and list pricing. Neither model is a blowout; each has clear territory it owns.
How to map the rounds to a buying decision
If the workload is dominated by patching real repositories (fixing GitHub issues, editing across files, driving a terminal to run tests) Opus 4.5 is the higher-scoring choice. Claude Opus 4.5 delivers a state-of-the-art 80.9% on SWE-bench, outperforming Gemini 3 Pro (76.2%) and GPT 5.1 (76.3%) , and on Terminal-Bench, Opus 4.5 scores 59.3%, ahead of Gemini 3 Pro (54.2%) . The gap is large enough that a coding agent shipping into production should default to Opus 4.5.
If the workload is competitive-programming-style novel code generation, the pattern reverses. Gemini 3 Pro shows a commanding lead in algorithmic problem-solving, achieving an Elo rating of 2,439 on LiveCodeBench Pro. This is nearly 200 points higher than GPT-5.1 (2,243), indicating superior skill in generating novel and efficient code.
If the workload is long-horizon autonomous operation, the kind of multi-step planning where a model has to stay coherent over thousands of decisions, Gemini 3 Pro is the defensible pick. Claude Opus 4.5 achieved an impressive final balance of $4,967.06 at 23% increase over Sonnet 4.5 ($3849.74). While an impressive result, Gemini 3 Pro currently leads on this benchmark with a final balance of $5,478.16.
On price parity
The list-price gap is the easiest fact to state and the easiest one to mis-apply. Pricing is now $5/$25 per million tokens for Opus 4.5, and Gemini 3 Pro sits at $2/$12 for prompts under 200K. On sticker, Gemini 3 Pro is 2.1–2.5x cheaper.
Two structural factors compress that gap on real workloads. First, set to a medium effort level, Opus 4.5 matches Sonnet 4.5’s best score on SWE-bench Verified, but uses 76% fewer output tokens. At its highest effort level, Opus 4.5 exceeds Sonnet 4.5 performance by 4.3 percentage points, while using 48% fewer tokens. Since output is priced 5x higher than input across the Claude lineup, output-token efficiency compounds directly into per-task cost. Second, Claude Opus 4.5 delivers high-quality code and excels at powering heavy-duty agentic workflows with GitHub Copilot. Early testing shows it surpasses internal coding benchmarks while cutting token usage in half . For output-heavy coding-agent workloads the effective sticker gap is materially smaller than 2.5x.
For token-heavy input workloads (long-context document analysis, RAG pipelines with large retrieved contexts) Gemini 3 Pro’s list price wins cleanly. For output-heavy agent workloads with cache reuse, the math is closer to a tie.
On reasoning-effort controls
Both vendors now expose per-request reasoning depth as a first-class API parameter. Anthropic introduced Effort parameter. A new API control for tuning the thinking depth per request. Low effort for quick tasks, high effort for complex reasoning. You control the budget without switching models. Gemini 3 Pro exposes an equivalent low/medium/high control for the same tradeoff. The mechanic is now standard at the frontier. The differentiator is the shape of the quality/cost curve each vendor exposes, which favors Opus 4.5 on coding tasks where reasoning depth translates most directly to first-attempt correctness.
On the ARC-AGI-2 caveat
The abstract-reasoning round deserves a footnote. On the GA Gemini 3 Pro model, ARC-AGI-2 (Visual reasoning puzzles; ARC Prize Verified) Gemini 3 Pro 31.1%, Gemini 2.5 Pro 4.9%, Claude Sonnet 4.5 13.6%, GPT-5.1 17.6%. On that number, Opus 4.5’s 37.6% wins the round. Google’s February 2026 Gemini 3.1 Pro preview posted Gemini 3.1 Pro scores 77.1% on ARC-AGI-2 (up from 31.1% for Gemini 3 Pro) , a step-change result that flips the round if a team is willing to build on a preview endpoint. As of the test date, Gemini 3.1 Pro remains in preview with no GA date, so the round is scored on the GA numbers a production buyer can commit to.
On long-context recall
Both models list a 1M-token window, but the useful comparison is retrieval accuracy inside that window, not the raw ceiling. Gemini 3 Pro demonstrates strong recall, scoring 77.0% on the 128k average context length test. For larger context, 1M, Gemini 3 Pro outpaces the Gemini 2.5 Pro model by 9.9%. Anthropic exposes Opus 4.5’s 1M window via a beta header and publishes less detailed needle-in-a-haystack data, so on the published evidence Gemini 3 Pro carries this round.
Multimodal and agent-tool notes
Beyond the scored rounds, two capability differences are worth pricing into a long-horizon commitment. Gemini 3 Pro ships stronger multimodal grounding: Multimodality : High scores in both MMMU-Pro (81.0%) and Video-MMMU (87.6%). This suggest a great ability to process and reason across temporal and spatial dimensions simultaneously. This makes it highly effective for integrated tasks like analyzing video lectures or complex UI screenshots . Opus 4.5 ships better agent-orchestration primitives, including Auto-compaction. When sessions hit 95% of the context window, the model automatically compacts earlier messages while preserving the full conversation thread. This created effectively unbounded session length for long engineering tasks.
Neither capability shows up in the headline score, but both change what a team can build on top of the model. Video-heavy analysis workflows favor Gemini 3 Pro; extended coding sessions and multi-agent orchestration favor Opus 4.5.
- https://www.anthropic.com/news/claude-opus-4-5
- https://www.vellum.ai/blog/claude-opus-4-5-benchmarks
- https://simonwillison.net/2025/Nov/18/gemini-3/
- https://www.vellum.ai/blog/google-gemini-3-benchmarks
- https://platform.claude.com/docs/en/about-claude/pricing
- https://ai.google.dev/gemini-api/docs/pricing
Priya Raman runs the Top AI Tracker test bench. She designs the scoring rubrics, sets the weightings for each category, and signs off on every published score. Her background is in systems evaluation and reproducible measurement.