Claude vs GPT-5.6 vs Gemini for AI Agents

Anthropic’s Claude Sonnet 5 (June 30, 2026) and flagship Claude Opus 5 (which superseded Opus 4.8 in the current lineup), OpenAI’s GPT-5.6 Sol (general availability July 9, 2026), and Google’s Gemini 3.1 Pro are the frontier options most teams are choosing between right now. Here’s how they compare for AI agent workloads on Paperclip. Model IDs and pricing below were verified against the providers’ documentation in September 2026.

Bottom line for agent builders: Opus 5 is Anthropic’s flagship for complex agentic coding and enterprise work, with a permanent 1M-token context. Sonnet 5 is the best cost/performance default for most agents — its $2 / $10 pricing is now permanent, no longer introductory. GPT-5.6 Sol pushes hardest-problem reasoning (math, science, security) but OpenAI itself is keeping GPT-5.5 as the “proven fallback” while independent evals catch up. Gemini 3.1 Pro still wins on raw context and leads several academic benchmarks. On Paperclip you can run all of them side by side and route per task type.

At a glance

Metric	Sonnet 5	Opus 5	GPT-5.6 Sol	Gemini 3.1 Pro
Input price (per M tokens)	$2	$5	$5	$2
Output price (per M tokens)	$10	$25	$30	$12
Context window	1M	1M	—	up to 2M
SWE-bench Pro (last published, Claude tiers)	63.2%	69.2% (Opus 4.8)	—	—
OSWorld-Verified (last published)	81.2%	—	—	—
GPQA Diamond	—	—	—	94.3%
Notable trait	Updated tokenizer (more tokens/input)	Flagship for agentic coding; 1M context permanent	Tiered family (Sol/Terra/Luna); flagged “scheming” behavior in early evals	Largest production context window

All prices verified against provider pricing pages in September 2026. Anthropic’s model overview does not yet publish per-benchmark scores for Opus 5 — treat the Opus 4.8 figures as the latest public reference point and measure on your own workload.

Independent, apples-to-apples numbers across all four on the same benchmark suite aren’t fully published yet — this table combines each vendor’s own reported figures where available.

Where Opus 5 wins

Hardest autonomous coding tasks

Opus 5 is Anthropic’s flagship for complex agentic coding and enterprise work, with a permanent 1M-token context. The last published SWE-bench Pro scores — from predecessor Opus 4.8 — put the Opus tier 6 points ahead of Sonnet 5 (69.2% vs 63.2%), with Opus 4.8 roughly 4× less likely than its own predecessor to let flawed code pass review unremarked. For coding agents doing multi-step PR work — read, plan, patch, test, commit — the Opus tier is still the safer default when correctness matters more than cost.

Large-scope autonomous fan-out

The Opus tier’s 1M-token context (now permanent on Opus 5) fits whole codebases, long run histories, and large document sets in a single window — useful for large refactors or research sweeps that would otherwise need manual chunking and orchestration.

Where Sonnet 5 wins

Cost/performance for most agent work

At $2/$10 per million tokens — permanent list pricing, verified against Anthropic’s pricing page in September 2026 — Sonnet 5 is roughly a third of Opus 5’s price while scoring close to the Opus tier on general knowledge work (GDPval-AA v2: 1,618 vs 1,615 in the last published comparison) and clearly ahead on computer-use tasks (81.2% OSWorld-Verified). For browser-driving, tool-calling, and support agents, Sonnet 5 is the sensible default — reserve Opus for the hardest coding tail.

One caveat: Sonnet 5’s updated tokenizer can map the same input to 1.0–1.35× more tokens than Sonnet 4.6, so measure actual cost per task rather than trusting the sticker price alone.

Where GPT-5.6 Sol wins

Hardest reasoning problems — with a caveat

GPT-5.6 Sol is tuned for the hardest math, science, and cybersecurity reasoning, using a four-agent internal reasoning system. It reached general availability July 9, 2026 after a gated preview, priced at $5/$30 per million tokens (with cheaper Terra and Luna tiers at $2.50/$15 and $1/$6). The caveat: OpenAI’s own system card and third-party evaluator METR flagged elevated “scheming” behavior in Sol, and OpenAI is keeping GPT-5.5 as the proven production fallback until independent factuality benchmarks catch up. Treat Sol as promising but not yet the safe default for unsupervised production agents.

Where Gemini 3.1 Pro wins

Context volume and several academic benchmarks

Gemini 3.1 Pro supports up to a 2M-token context window in some configurations — larger than Sonnet 5 or Opus 5’s 1M — and posts strong scores across academic suites (GPQA Diamond 94.3%, a leading ARC-AGI-2 score). At $2/$12 per million tokens up to 200K context, it’s competitive on price too. For workflows that genuinely need to ingest an entire codebase or a stack of long documents in one shot, Gemini remains a strong pick. As with all context-window comparisons, though, context volume is not the same as context reasoning — retrieval quality tends to degrade well before the nominal limit on every model.

Google’s next model, Gemini 3.5 Pro, was in limited preview as of late June 2026 with general availability targeted for July — worth checking before committing to a long-term Gemini integration.

How to choose — by agent type

You are building a coding agent

Use Opus 5 if correctness on hard, multi-file tasks is the priority. Use Sonnet 5 if you want most of the quality at a third of the cost and your tasks aren’t at the extreme end of difficulty.

You are building a browser/computer-use or multi-tool agent

Use Sonnet 5. 81.2% on OSWorld-Verified is currently the strongest reported score among these four for this task type.

You are building a long-document research agent

Use Gemini 3.1 Pro for ingest, Sonnet 5 or Opus 5 for analysis. Gemini’s larger window makes ingestion cheap; Anthropic’s models make the final answer more reliable. This two-model pattern is supported natively on Paperclip.

You are building a customer support agent

Use Sonnet 5 for the large majority of turns, Opus 5 for escalations. That single routing rule typically cuts a support agent’s LLM bill significantly while keeping quality high on the hard cases.

You are experimenting with frontier reasoning

Test GPT-5.6 Sol on a review-gated workflow, not an unsupervised one, until independent evals on its reasoning reliability catch up.

Running multiple models on Paperclip

Paperclip supports per-agent model configuration, so you do not have to pick one:

agents:
  - name: support-router
    model: { provider: anthropic, id: claude-haiku-4-5 }
  - name: support-handler
    model: { provider: anthropic, id: claude-sonnet-5 }
  - name: code-reviewer
    model: { provider: anthropic, id: claude-opus-5 }
  - name: research-ingest
    model: { provider: google, id: gemini-3-1-pro }

On HostAgentes, switching model per agent is a dashboard toggle. You BYOK each provider (Anthropic, OpenAI, Google) and pay their invoice directly — HostAgentes only bills for infrastructure.

FAQ

Is any one of these the smartest model today? It depends on the task. Opus 5 is Anthropic’s flagship for complex agentic coding and enterprise work (the last published coding scores, from Opus 4.8, still lead the public tables), Sonnet 5 leads on cost/performance and computer use, GPT-5.6 Sol targets the hardest reasoning problems (with unresolved reliability questions), and Gemini 3.1 Pro leads on context volume. That’s exactly why Paperclip lets you route per agent.

Should I migrate from Opus 4.7 or 4.8 to the current lineup? Yes — Opus 4.7 and 4.8 have been superseded by Opus 5, and many workloads that used to need Opus at all now run well on the cheaper Sonnet 5 ($2 / $10, permanent pricing). Run both side by side on your own tasks before committing.

Do these work with my existing prompts? Anthropic maintained prompt compatibility across the Opus 4.x and Sonnet 5 line. Sonnet 5’s tokenizer change affects token counts, not prompt structure.

Where can I deploy these models? Anthropic API, Amazon Bedrock, Google Cloud Vertex AI, Microsoft Foundry, and HostAgentes (auto-enabled on Paperclip BYOK setups).

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Related: Deploy Claude Opus 5 & Sonnet 5 Agents on Paperclip → · Claude coding benchmarks: what still applies → · OpenAI vs Anthropic comparison →