by Google · Gemini 3 family · best for frontier reasoning + long-context on Google Cloud
Gemini 3.1 Pro is Google DeepMind's flagship reasoning model, launched 2026-02-19 in preview to validate the release before general availability. As of 2026-05-28 it remains the headline model in the Gemini app (Google AI Pro and Ultra) and the top reasoning option on the Gemini API and Vertex AI, even though its API/Vertex surface is still governed by Pre-GA Offerings Terms. It posts the highest public GPQA Diamond score of any proprietary model (94.3%, no tools), pairs that with a real 1M-token context (2M on Vertex for enterprise), and grounds answers in live Google Search. For a buyer: if you want frontier reasoning plus the deepest enterprise-cloud and live-data integration, this is Google's answer — accept that the API is technically still pre-GA. - Provider: Google (DeepMind) - Released: 2026-02-19 (preview; no GA date announced) - Status: preview (Pre-GA terms on API/Vertex; production-default in the consumer Gemini app) - Context window: 1,048,576 tokens (2,097,152 / 2M on Vertex AI) - Max output: 65,536 tokens - Modalities: text, image, audio, video in; text out - Knowledge cutoff: January 2025 - Headline price: $2.00 in / $12.00 out per 1M tokens (<=200K prompt)
| Benchmark | Score | Source |
|---|---|---|
| Humanity's Last Exam | 44.4% | deepmind.google 2026-02-19T00:00:00.000Z |
| MMLU | 92.6% | deepmind.google 2026-02-19T00:00:00.000Z |
| MMMU | 80.5% | deepmind.google 2026-02-19T00:00:00.000Z |
| TAU-bench | 90.8% | deepmind.google 2026-02-19T00:00:00.000Z |
| LMArena Elo | 1501 | facebook.com 2026 |
| GPQA Diamond | 94.3% | deepmind.google 2026-02-19T00:00:00.000Z |
| LiveCodeBench | 2887% | deepmind.google 2026-02-19T00:00:00.000Z |
| Terminal-Bench | 68.5% | deepmind.google 2026-02-19T00:00:00.000Z |
| MRCR Long Context | 84.9% | deepmind.google 2026-02-19T00:00:00.000Z |
| SWE-bench Verified | 80.6% | deepmind.google 2026-02-19T00:00:00.000Z |
| Artificial Analysis Index | 57 | artificialanalysis.ai 2026-05-28T00:00:00.000Z |
Six personas, six verdicts — the same panel that reviews every product on TopReviewed.
“The strongest case yet to standardize on Google's AI stack — if we can live with a flagship that's still technically pre-GA.”
Gemini 3.1 Pro gives a CTO frontier reasoning plus the deepest enterprise integration on the market: Vertex VPC-SC, CMEK, residency controls, audit logging, and Workspace/BigQuery grounding no rival matches. The 1M-2M context removes RAG complexity for many workloads. Two strategic risks: it remains under Pre-GA terms (support and stability caveats), and adopting Vertex deepens Google Cloud lock-in. At $2/$12 with cache discounts and the 2M context, long-context TCO beats Claude Opus and GPT-5 Pro. Roadmap confidence is high given 3.5 Pro is already incoming.
“Google's wedge is live data plus long context — 3.1 Pro turns the Search and Workspace moat into a model-level advantage.”
In market terms, 3.1 Pro competes at the very top on reasoning (GPQA 94.3%) while owning a differentiation axis rivals can't copy: native Google Search grounding and Workspace/BigQuery integration. That positions it as the default for any org already inside Google's ecosystem and for use cases where freshness and citation matter. Its competitive moat is distribution (the Gemini app, Workspace, Android, Cloud) more than raw benchmark leads, several of which Opus 4.6 and GPT-5.3-Codex contest. Market timing is strong, but 3.5 Pro looming may stall procurement decisions.
“Cleanest frontier pricing in the category — until a prompt crosses 200K and the per-token rate quietly doubles.”
$2/$12 standard is competitive with GPT-5 and ~40% under Claude Opus 4.7 for long-context work. Explicit caching cuts input reads to $0.20, and batch halves rates. The catch is the two-tier model: above 200K tokens, input/output jump to $4/$18, and cached storage runs $4.50/1M tokens/hour — easy to under-model in RAG and streaming pipelines. Thinking tokens bill as output and can balloon spend on Deep Think. Vertex billing folds into existing Google Cloud invoices, simplifying procurement. Predictable once the tier boundary and thinking-token behavior are understood.
“Function calling and structured output just work, and 1M context lets me skip half my RAG plumbing.”
For a builder, the Gemini API and Vertex surface is the smoothest Google has shipped — clean SDKs (Python, TS, Go, Java, Dart), reliable function calling, response-schema structured output, built-in code execution, and Search grounding. The 1M-2M context collapses many agent loops into a single call. Genkit and Google ADK ease orchestration. Friction points: preview-tier RPD caps until spend gates clear, and high TTFT makes tight interactive loops painful. AI Studio's prompt history helps debug tool calls. Migration across Gemini tiers is a model-name swap.
“Brilliant on hard problems and live questions; just don't expect it to answer fast in Deep Think.”
In the Gemini app on AI Pro/Ultra, 3.1 Pro is genuinely excellent for research, planning, and analysis, and Search grounding makes it strong on current events where most rivals guess. Conversation quality is high and multimodal understanding (PDFs, images, video) is a daily advantage. Downsides users feel: Deep Think latency (10-30s), stricter refusals on edgy prompts than Claude or ChatGPT, and a tone that reads slightly clinical. The 2026 UX overhaul (native macOS app, cleaner mobile) fixed many prior complaints; Trustpilot remains mixed, mostly about caps and policy.
“A 'flagship' still under Pre-GA terms, with a 200K price cliff and TTFT north of 30 seconds — read the asterisks.”
The GPQA 94.3% headline is real, but several touted leads are contested: Opus 4.6 edges SWE-bench, GPT-5.3-Codex leads specialized coding, and 3.5 Flash beats 3.1 Pro on agentic benchmarks Google itself publishes. "Released" overstates it — this is preview validating before GA, with limited support. Long-context marketing glosses over MRCR recall decay near 1M and the doubled >200K pricing. AA hallucination data on the family is non-trivial, so Search grounding is doing real work to cover knowledge gaps. None of this makes it bad — it makes the "best at everything" framing marketing, not fact.
- Long-document agent workflows: legal review, research synthesis, codebase-wide refactors that exploit 1M-2M context. - Frontier coding agents needing both SWE-bench strength and reliable tool/function calling. - Scientific and research assistants where GPQA-class reasoning is the deciding factor. - Multimodal pipelines fusing PDFs, screenshots, audio, and video in one prompt. - Google Cloud enterprises wanting Vertex governance plus Workspace and BigQuery grounding.
Not formally. It launched 2026-02-19 in preview to validate before GA, and as of 2026-05-28 the API/Vertex surface is still under Pre-GA Offerings Terms. It is, however, the production-default model in the consumer Gemini app.
Prompts over 200K tokens bill at $4.00 input / $18.00 output per 1M (vs $2/$12 under 200K). Cached reads rise to $0.40 and storage is $4.50/1M tokens/hour.
The extended 2M context is rolling out on Vertex AI for enterprise; the standard Gemini API exposes 1M.
No for paid API and Vertex inputs. The free AI Studio tier may use inputs to improve products; opt-out is available.
3.5 Flash beats 3.1 Pro on Terminal-Bench, MCP Atlas, and CharXiv at lower cost and 1.5x speed. Use Pro when pure reasoning, HLE-class problems, or long-context recall dominate.
Google Search grounding gives 5,000 free prompts/month (shared across Gemini 3), then $14 per 1,000 queries — the deepest real-time integration of any frontier model.
No. Gemini is closed-weights, API/Vertex only.
Does not train on API inputs by default
Last verified 2026-05-27