# Claude Opus 4.7 vs Gemini 3 Pro (2026): side-by-side comparison

Source: [GLAD-AI-TOR](https://glad-ia-tor.com) · Full page: https://glad-ia-tor.com/vs/claude-opus-4-7-vs-gemini-3-pro
Arena: llm-models · Crowd scores are live visitor verdicts (one per person per tool, never paid, Bayesian-smoothed).

## At a glance

| | Claude Opus 4.7 | Gemini 3 Pro |
|---|---|---|
| Price | $25/1M out | $12/1M out (prompts ≤200K) |
| Crowd score | 57% (3 votes) | 57% (3 votes) |
| provider | Anthropic | Google (DeepMind) |
| contextWindow | 1M tokens (128K max output) | 1M tokens (64K output) |
| priceIn | $5/1M in | $2/1M in (prompts ≤200K) |
| priceOut | $25/1M out | $12/1M out (prompts ≤200K) |
| modalities | text + image input (up to 2576px), text output | text, image, audio, video in; text out |
| openWeights | no | no |
| reasoning (1-5) | 4.5 | 4.5 |
| coding (1-5) | 4.5 | 4.5 |
| writing (1-5) | 4.5 | 3.5 |
| speed (1-5) | 2.5 | 3.5 |
| valueForMoney (1-5) | 3 | 4 |

### Claude Opus 4.7

> Anthropic's April 2026 Opus: 87.6% SWE-bench Verified, 1M context, high-res vision, now behind Opus 4.8

Strengths:
- 87.6% SWE-bench Verified (up from 80.8% on Opus 4.6) and 64.3% SWE-bench Pro at launch, ahead of GPT-5.4 (57.7%) and Gemini 3.1 Pro (54.2%)
- 1M-token context window and 128K max output at flat $5/$25 pricing with no long-context premium (300K output via Batch API beta)
- First Claude with high-resolution vision: accepts images up to 2576px on the long edge with pixel-accurate coordinates, ~3x prior detail
- Standout code review: finds more real bugs with stronger cross-file reasoning than rivals in independent tests, and 21% fewer document-reasoning errors than Opus 4.6

Weaknesses:
- New tokenizer inflates token counts roughly 30% for the same text versus pre-4.7 models (per Anthropic's own docs), raising effective per-request cost despite the unchanged sticker price
- Very verbose in agentic use: one benchmark found GPT-5.5 used 72% fewer output tokens on equivalent coding tasks, and reviewers call its narration over-communicative
- Breaking API changes bite migrators: temperature/top_p/top_k and thinking budget_tokens now return 400 errors, and thinking text is hidden by default

Verdict: Choose Opus 4.7 only if you are already pinned to it for reproducibility: Opus 4.8 costs the same $5/$25, keeps an identical API surface, and outperforms it, making it the better default for new projects. It remains a very strong pick for agentic coding, code review and 1M-context document work, and is a clear upgrade over Opus 4.6. Teams migrating from 4.6 should budget for breaking API changes and a tokenizer that yields roughly 30% more tokens per prompt. Cost-sensitive users should look at Sonnet 5, which delivers near-Opus quality at $3/$15 (intro $2/$10 through August 31, 2026).

Full review: https://glad-ia-tor.com/tool/claude-opus-4-7 · Markdown: https://glad-ia-tor.com/tool/claude-opus-4-7.md

### Gemini 3 Pro

> Google's Nov 2025 frontier model: 1M context, first past 1500 Elo on LMArena, replaced by Gemini 3.1 Pro.

Strengths:
- Topped LMArena at launch with a record 1501 Elo and scored 91.9% on GPQA Diamond, state of the art at release
- ARC-AGI-2 at 31.1%, roughly 6x Gemini 2.5 Pro (4.9%) and nearly double GPT-5.1 (17.6%) at the time
- Best-in-class multimodal understanding: 81% MMMU-Pro, 87.6% Video-MMMU, with a 1M-token context window
- Strong agentic coding: 76.2% SWE-bench Verified, 54.2% Terminal-Bench 2.0, 1487 Elo on WebDev Arena

Weaknesses:
- Overconfident hallucinations: on AA-Omniscience it gave a wrong answer 88% of the time instead of declining, vs 48% for Claude Sonnet 4.5 (the-decoder)
- Sycophancy widely reported by reviewers (Zvi Mowshowitz: 'vast intelligence with no spine'); needs tight system prompts
- Tool-calling reliability issues in agent stacks: devs reported tool outputs dumped into the chat thread and more scaffolding needed than OpenAI/Anthropic models

Verdict: A landmark release that put Google back on top in late 2025, with a huge reasoning jump over Gemini 2.5 Pro and the best multimodal scores of its generation. As of mid-2026 there is no reason to choose it: Google shut it down on the API on March 9, 2026, and Gemini 3.1 Pro costs exactly the same while more than doubling ARC-AGI-2 performance (77.1% vs 31.1%). Teams on legacy deployments should migrate to 3.1 Pro, which the old model ID now points to anyway. Avoid it for hallucination-sensitive workloads unless you add grounding, a weakness reviewers flagged repeatedly.

Full review: https://glad-ia-tor.com/tool/gemini-3-pro · Markdown: https://glad-ia-tor.com/tool/gemini-3-pro.md

## More

Full llm-models ranking: https://glad-ia-tor.com/hall-of-fame/llm-models

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