# Gemini 3 Pro vs DeepSeek-V4 (2026): side-by-side comparison

Source: [GLAD-AI-TOR](https://glad-ia-tor.com) · Full page: https://glad-ia-tor.com/vs/gemini-3-pro-vs-deepseek-v4
Arena: llm-models · Crowd scores are live visitor verdicts (one per person per tool, never paid, Bayesian-smoothed).

## At a glance

| | Gemini 3 Pro | DeepSeek-V4 |
|---|---|---|
| Price | $12/1M out (prompts ≤200K) | $0.87/1M out |
| Crowd score | 57% (3 votes) | 57% (3 votes) |
| provider | Google (DeepMind) | DeepSeek |
| contextWindow | 1M tokens (64K output) | 1M tokens (384K max output) |
| priceIn | $2/1M in (prompts ≤200K) | $0.435/1M in (cache hit $0.003625) |
| priceOut | $12/1M out (prompts ≤200K) | $0.87/1M out |
| modalities | text, image, audio, video in; text out | text only |
| openWeights | no | yes |
| reasoning (1-5) | 4.5 | 4.5 |
| coding (1-5) | 4.5 | 4.5 |
| writing (1-5) | 3.5 | 3.5 |
| speed (1-5) | 3.5 | 3 |
| valueForMoney (1-5) | 4 | 5 |

### 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

### DeepSeek-V4

> Open-weights 1.6T MoE with 1M-token context and near-frontier agentic coding at $0.87 per 1M output tokens

Strengths:
- 1M-token context window (8x the 128K of V3.2) with up to 384K output tokens, standard on the official API
- Aggressive pricing: $0.435/$0.87 per 1M tokens (V4-Pro), roughly 28.7x cheaper per output token than Claude Opus 4.8; cache-hit input drops to $0.003625/1M (over 99% discount)
- MIT-licensed open weights for both V4-Pro and V4-Flash on Hugging Face: commercial use, fine-tuning and redistribution allowed
- Open-source SOTA on agentic coding: 80.6 on SWE-bench Verified (Think Max config), tied with Gemini 3.1 Pro, plus Codeforces rating 3206 (~rank 23 vs humans)

Weaknesses:
- Intermittent malformed tool calls: function calls sometimes emitted as plain text in content instead of the tool_calls field (GitHub issue deepseek-ai #1244)
- Thinking mode breaks long multi-turn tool-call chains with 400 errors in agent frameworks (OpenClaw issue #72044, fix still incomplete)
- Developers report it fabricating nonexistent APIs in custom codebases and acting on hallucinated user input in agent loops

Verdict: Choose DeepSeek-V4 if you want near-frontier reasoning and agentic coding at 3x to nearly 30x below Claude Opus or GPT-5.5 pricing, or if MIT-licensed weights for self-hosting and fine-tuning matter to you. It is a decisive upgrade over V3.2: 8x longer context, far cheaper long-context inference and stronger coding, and the legacy deepseek-chat/reasoner endpoints are deprecated on July 24, 2026 anyway. Avoid it for production agents that depend on rock-solid multi-turn tool calling, where users still report malformed tool calls and fabricated APIs, and for any vision or audio work since it is text only. Latency-sensitive apps should also test first, as its verbosity and mid-pack 54.6 tok/s output speed offset some of the cost advantage, and budget for the peak-hour price doubling arriving with the official mid-July release.

Full review: https://glad-ia-tor.com/tool/deepseek-v4 · Markdown: https://glad-ia-tor.com/tool/deepseek-v4.md

## More

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

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