# Claude Sonnet 5 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-sonnet-5-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 Sonnet 5 | Gemini 3 Pro |
|---|---|---|
| Price | $15/1M out ($10 intro until 2026-08-31) | $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 | $3/1M in ($2 intro until 2026-08-31) | $2/1M in (prompts ≤200K) |
| priceOut | $15/1M out ($10 intro until 2026-08-31) | $12/1M out (prompts ≤200K) |
| modalities | text, vision (image input, 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) | 4 | 3.5 |
| valueForMoney (1-5) | 4.5 | 4 |

### Claude Sonnet 5

> Anthropic's most agentic Sonnet: near Opus 4.8 quality on coding and agents at $3/$15 with 1M context

Strengths:
- Large agentic gains over Sonnet 4.6: Terminal-Bench 2.1 80.4% vs 67.0%, OSWorld-Verified 81.2% vs 78.5%, SWE-bench Pro 63.2% vs 58.1%
- Matches Opus 4.8 on knowledge work (GDPval-AA v2: 1,618 vs 1,615) and nearly ties it on Humanity's Last Exam with tools (57.4% vs 57.9%) at 60% of Opus 4.8 pricing (40% during the intro window)
- 1M token context window and 128K max output; introductory pricing of $2/$10 per 1M tokens through Aug 31, 2026
- Persistent self-verifying agent behavior: hands-on reviews note it tests its own code and iterates on hard problems until solved, unlike Sonnet 4.6

Weaknesses:
- New tokenizer inflates token counts roughly 30% for the same text (1.0-1.35x per Anthropic; ~1.4x English, ~1.28x Python measured by Simon Willison), raising effective cost despite the unchanged sticker price
- Verbose and token-hungry: ~$2.29 per task vs ~$1.20 for Sonnet 4.6 in independent tests (ranked 101st of 161 for cost efficiency); at high effort cost-per-task can exceed Opus 4.8
- Measurably slower than Sonnet 4.6 on small routine edits and prone to over-engineering simple tasks (CodeRabbit hands-on review)

Verdict: Choose Sonnet 5 if you run coding, terminal or computer-use agents and want near Opus 4.8 quality at Sonnet prices, especially during the $2/$10 intro window; it is a strict upgrade over Sonnet 4.6 at low and medium effort. Budget for the new tokenizer and its verbosity: real per-task costs run well above Sonnet 4.6, and at the highest effort levels Opus 4.8 can be the better deal per solved task. Avoid it for latency-sensitive small edits or pipelines that rely on temperature and top_p, which now error. Sonnet 4.6 remains the pragmatic pick for high-volume tiny-diff workloads.

Full review: https://glad-ia-tor.com/tool/claude-sonnet-5 · Markdown: https://glad-ia-tor.com/tool/claude-sonnet-5.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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