GPT-5.6 vs Gemini 3 Pro

Deployability decides this one before benchmarks: GPT-5.6 (Sol) is in a limited preview open only to a small set of vetted organizations, opened this way at the US government's request; Gemini 3 Pro is generally available on Vertex AI today. For most teams, that makes this a preview-vs-GA choice, and Gemini wins on what you can actually ship now.

This page compares the two on the levers a buyer controls: can you get it, what it costs, how it codes and reasons, whether it fits your compliance rules, and which ecosystem it plugs into. We skip benchmark chasing. The goal is a pick you can defend to your team and your auditor.

GPT-5.6 Sol targets the hardest coding and security work and adds new max and ultra reasoning modes. Gemini 3 Pro is Google's generally available flagship, running in your chosen Google Cloud region with no training on your data. Both are capable; access and governance decide the near-term winner.

GPT-5.6 (Sol) vs. Gemini 3 Pro: Side-by-Side

Availability: which can you deploy today?

Gemini 3 Pro is the only one of these two you can deploy today without a waitlist. It is generally available on Vertex AI now, pinned to your chosen Google Cloud region. GPT-5.6 is in a limited preview open only to a small set of vetted organizations, via API and Codex, opened this way at the US government's request.

For most teams, this settles near-term projects. You cannot build a launch plan around a model you cannot license. GPT-5.6 general availability is planned in the coming weeks, but no firm date is public.

If access matters more than a benchmark edge, Gemini 3 Pro is the deployable pick right now. Revisit GPT-5.6 once it reaches general availability and you can price and test it on your own tasks.

• Need to ship this quarter: Gemini 3 Pro is deployable now on Vertex AI.
• Not in the GPT-5.6 preview: you cannot use Sol yet.
• Planning ahead: track the GPT-5.6 GA date before committing.

Price: token costs side by side

Gemini 3 Pro is cheaper than GPT-5.6 Sol on both input and output at standard context. Gemini 3 Pro costs $2 per million input tokens and $12 per million output tokens up to 200K context. Sol costs $5 input and $30 output per million tokens.

Context length changes the Gemini math. Above 200K tokens, the whole request moves to $4 input and $18 output per million tokens. Watch your prompt sizes so a few long requests do not push your whole workload to the higher tier.

GPT-5.6's cheaper tiers narrow the gap once available. Terra costs $2.50 input and $15 output; Luna costs $1 input and $6 output. Luna undercuts Gemini 3 Pro on price, but you cannot use any GPT-5.6 tier outside the preview today.

• Gemini 3 Pro: $2 input / $12 output per million tokens up to 200K context.
• GPT-5.6 Sol: $5 input / $30 output per million tokens.
• Cheaper GPT-5.6 tiers: Terra $2.50/$15, Luna $1/$6, once available.

→ Compare per-token pricing for every AI model

Coding and reasoning

Both models handle serious coding and long-context reasoning, so the useful split is access to advanced modes. GPT-5.6 Sol targets the hardest coding and security research and adds new max and ultra reasoning modes, where ultra uses subagents to speed complex work. Gemini 3 Pro brings strong coding and long-context reasoning to agentic workflows on Vertex AI.

The GPT-5.6 reasoning modes are the standout feature, but they are locked behind the preview. You cannot test ultra mode on your own code unless your organization is in the vetted group. Gemini 3 Pro is testable today on Vertex AI, so you can measure real coding output against your tasks now.

For a near-term coding rollout, run a short pilot on Gemini 3 Pro and record accuracy, latency, and cost. Re-run the same pilot on GPT-5.6 once it is generally available. A one-week trial on your own code reveals more than any leaderboard.

• GPT-5.6 Sol: max and ultra reasoning modes for the hardest coding and security work.
• Gemini 3 Pro: strong coding and long-context reasoning, testable today on Vertex AI.
• You can pilot Gemini now; GPT-5.6 modes stay locked until GA.

Compliance: which fits regulated data?

Gemini 3 Pro on Vertex AI gives regulated teams a documented, region-pinned setup today. Vertex AI processes your data in your chosen Google Cloud region, does not use it for training, and carries SOC 2, ISO 27001, HIPAA eligibility, and a GDPR DPA. The consumer Gemini app is different: that data can be used for training, so keep regulated work on Vertex AI.

GPT-5.6 inherits OpenAI's platform coverage: SOC 2, ISO 27001, and a HIPAA BAA on the API and Enterprise, once a model is in scope. Because GPT-5.6 is still a preview, confirm the specific model is named in your BAA before sending any regulated data.

Region pinning is the non-obvious tiebreaker. If your rules require data to stay in a specific country, Gemini 3 Pro on Vertex AI lets you name the region up front. Verify GPT-5.6's data residency terms during your preview evaluation before you rely on them.

• Gemini 3 Pro on Vertex AI: SOC 2, ISO 27001, HIPAA-eligible, GDPR DPA, region-pinned, no training.
• GPT-5.6: inherits OpenAI SOC 2, ISO 27001, HIPAA BAA once in scope.
• Keep regulated work on Vertex AI, not the consumer Gemini app.

Ecosystem: which stack do you already live in?

Pick the model that fits the tools your team already uses. Gemini 3 Pro plugs into Vertex AI and Google Workspace, so teams on Google Cloud and Gmail get a short path to production. GPT-5.6 runs through the OpenAI API and Codex, which suits teams already building on OpenAI.

The ecosystem often decides the real cost of adoption. If your data, identity, and billing already sit in Google Cloud, Gemini 3 Pro avoids a second vendor and a new security review. If your developers live in Codex, GPT-5.6 keeps them in one workflow once they have preview access.

Map the model to your existing stack before you weigh token price. A cheaper model in a foreign ecosystem can cost more once you add integration, review, and training time.

• Gemini 3 Pro: Vertex AI plus Google Workspace, native to Google Cloud teams.
• GPT-5.6: OpenAI API and Codex, native to OpenAI-first teams.
• Ecosystem fit often outweighs a small token-price gap.

How to choose for your business

Choose on access first, then ecosystem, then price. If you must ship soon, Gemini 3 Pro is generally available on Vertex AI today. If you are in the GPT-5.6 preview and need max or ultra reasoning for hard coding, Sol is worth evaluating.

Match the model to your stack, not the hype. Google Cloud and Workspace teams get the shortest path with Gemini 3 Pro. OpenAI-first teams with preview access can keep developers in Codex on GPT-5.6.

Run a short pilot before you commit. Test coding accuracy, refusal behavior, latency, and cost on your own tasks. For most teams this stays a preview-vs-GA call, so the deployable model usually wins the quarter.

• Ship now, generally available: Gemini 3 Pro on Vertex AI.
• Advanced reasoning on hard coding, if you have preview access: GPT-5.6 Sol.
• Regulated and region-locked: Gemini 3 Pro on Vertex AI fits today.

The Verdict

Gemini 3 Pro wins for most businesses today on availability alone. It is generally available on Vertex AI, while GPT-5.6 stays in a restricted preview at the US government's request. You cannot build a deadline around a model you cannot license.

Gemini 3 Pro also leads on standard-context price, at $2 input and $12 output versus Sol's $5 and $30. GPT-5.6's cheaper Terra and Luna tiers narrow the gap, but only once you can access them.

Compliance favors Gemini 3 Pro for near-term regulated work. Vertex AI is region-pinned with no training on your data, SOC 2, ISO 27001, HIPAA eligibility, and a GDPR DPA today. GPT-5.6 inherits OpenAI's coverage, but you must confirm the model is named in your BAA before sending regulated data.