Key Takeaways
- GPT-5.5 leads Terminal-Bench 2.0 at 82.7%, compared to Claude Opus 4.7 at 69.4% and Gemini 3.1 Pro at 68.5%.
- Claude Opus 4.7 wins SWE-bench Pro at 64.3%, ahead of GPT-5.5 (58.6%) and Gemini 3.1 Pro (54.2%), making it the strongest model for real GitHub issue resolution.
- Gemini 3.1 Pro achieved 77.1% on ARC-AGI-2, more than double Gemini 3 Pro’s 31.1%, the largest single-generation reasoning jump recorded in any frontier model family.
- Gemini 3.1 Pro is the cheapest option at $2.00 input / $12.00 output per million tokens (under 200K), versus GPT-5.5’s $5.00 / $30.00 and Claude Opus 4.7’s $5.00 / $25.00.
- Claude Opus 4.7 leads MCP-Atlas at 77.3%, the benchmark most directly testing multi-turn tool-calling, ahead of Gemini 3.1 Pro (73.9%) and GPT-5.4 (68.1%).
- GPT-5.5 is the first fully retrained OpenAI base model since GPT-4.5, released April 23, 2026, with native unified multimodal processing across text, images, audio, and video.
- All three models support 1M-token context windows in their APIs, though GPT-5.5 drops to 400K inside Codex.
- GPT-5.5 uses 72% fewer output tokens on equivalent agentic coding tasks compared to Claude Opus 4.7, which significantly affects API costs at high volume.
- No single model dominates every category: GPT-5.5 wins at terminal-based autonomy, Claude Opus 4.7 wins at complex multi-file code and tool orchestration, and Gemini 3.1 Pro wins at cost efficiency and novel reasoning.
Picking the right model for an agentic workflow in mid-2026 is no longer straightforward. Three months ago the field looked different. Now you have GPT-5.5 (April 23, 2026), Claude Opus 4.7 (April 16, 2026), and Gemini 3.1 Pro (February 19, 2026) all jostling for the top spot, each legitimately winning in different categories.
This article breaks down where each model actually performs well, where it falls short, and who should reach for which one. All benchmark numbers cited here come from published evaluations from OpenAI, Anthropic, Google DeepMind, and independent testing labs. No figures are invented.
Quick Comparison Table
| Factor | GPT-5.5 | Claude Opus 4.7 | Gemini 3.1 Pro |
|---|---|---|---|
| Release Date | April 23, 2026 | April 16, 2026 | February 19, 2026 |
| Context Window | 1M tokens (API), 400K (Codex) | 1M tokens | 1M tokens |
| API Input Price (per 1M tokens) | $5.00 | $5.00 | $2.00 (under 200K) |
| API Output Price (per 1M tokens) | $30.00 | $25.00 | $12.00 (under 200K) |
| Terminal-Bench 2.0 | 82.7% | 69.4% | 68.5% |
| SWE-bench Verified | ~72% | 87.6% | 80.6% |
| SWE-bench Pro | 58.6% | 64.3% | 54.2% |
| ARC-AGI-2 | Not published | Not published | 77.1% |
| MCP-Atlas (tool use) | ~68% | 77.3% | 73.9% |
| GPQA Diamond | 94.4% (GPT-5.4 Pro) | 94.2% | 94.3% |
| OSWorld-Verified | 78.7% | 78.0% | Not published |
| Best for | Terminal agents, speed | Multi-file code, tool calls | Cost efficiency, reasoning |
What Is GPT-5.5?
GPT-5.5 is OpenAI’s first retrained base model since GPT-4.5, released in February 2025. Unlike GPT-5.4 and the intermediate variants, GPT-5.5 is not a fine-tune or patch. It is a ground-up retraining focused on agentic performance, computer use, and sustained multi-step task completion.
The architecture change that matters most for agentic workflows: GPT-5.5 processes text, images, audio, and video in a single unified pass rather than the stitched-together multimodal pipeline that existed before. In practice, this means the model can reason about mixed-media context without the seam artifacts that sometimes caused earlier models to lose track of images mid-conversation.
On the agentic side, OpenAI leaned heavily into terminal and computer-use scenarios. GPT-5.5 scores 82.7% on Terminal-Bench 2.0, a benchmark testing complex CLI workflows that require planning, iteration, and tool coordination. That is a 13-point lead over Claude Opus 4.7’s 69.4%. On OSWorld-Verified, which tests real computer environment operation, GPT-5.5 hits 78.7%, edging Claude Opus 4.7 at 78.0%.
The model also shows significant token efficiency gains. According to published analysis, GPT-5.5 uses approximately 72% fewer output tokens than Claude Opus 4.7 on equivalent Codex tasks, which meaningfully reduces costs at high volume despite its higher per-token output price.
Pricing sits at $5.00 per million input tokens and $30.00 per million output tokens, double GPT-5.4’s rate. For long-prompt requests above 272K input tokens, pricing jumps to 2x input and 1.5x output for the full session. GPT-5.5 is available to Plus, Pro, Business, and Enterprise ChatGPT users, with GPT-5.5 Pro available on top tiers. Free users remain on GPT-5.3 Instant.
What Is Claude Opus 4.7?
Claude Opus 4.7 is Anthropic’s latest Opus-class model, released April 16, 2026, one week before GPT-5.5. It is a direct successor to Opus 4.6 with targeted improvements in advanced software engineering, vision quality, and agentic self-verification.
The headline benchmark improvement is on SWE-bench Verified, where Opus 4.7 jumps from 80.8% to 87.6%, a nearly 7-point gain that places it clearly ahead of Gemini 3.1 Pro (80.6%). On SWE-bench Pro, the harder multi-language variant measuring real-world GitHub issue resolution across multi-file codebases, Opus 4.7 climbs from 53.4% to 64.3%, surpassing both GPT-5.5 (58.6%) and Gemini (54.2%).
Vision is meaningfully improved. Opus 4.7 supports 2,576-pixel image support on the long edge, roughly 3.75 megapixels, more than three times the resolution of prior Claude models. For agentic tasks involving screenshots, UI navigation, or document understanding, this matters.
Two new features directly target agentic use: a new “xhigh” effort level between “high” and “max” for finer reasoning control, and task budgets in public beta that let developers set token ceilings on individual agentic subtasks. Anthropic also says Opus 4.7 verifies its own outputs before reporting back, which addresses one of the most consistent complaints about frontier models running extended pipelines: shallow fixes that pass automated tests but fail deeper review.
Pricing stays at $5.00 per million input tokens and $25.00 per million output tokens, unchanged from Opus 4.6. One caveat: an updated tokenizer can map the same input to roughly 1.0 to 1.35 times more tokens depending on content type, so effective costs per task may edge up. The 1M-token context window is maintained.
Opus 4.7 is available through the Anthropic API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry.
What Is Gemini 3.1 Pro?
Gemini 3.1 Pro is Google DeepMind’s current frontier model, released in preview on February 19, 2026. It is the most recent of the three models in this comparison by release date, though the two Anthropic and OpenAI releases that followed it have drawn more attention in the developer community.
The defining achievement of Gemini 3.1 Pro is its ARC-AGI-2 score. It hits 77.1%, compared to 31.1% for Gemini 3 Pro. That is more than a doubling of performance on a benchmark specifically designed to test novel reasoning that cannot be memorized from training data. No other frontier model family has shown a gain of that magnitude in a single generation on ARC-AGI-2.
On competitive coding, Gemini 3.1 Pro scores 2887 Elo on LiveCodeBench. On scientific coding, it reaches 59% on SciCode. On graduate-level science questions, it hits 94.3% on GPQA Diamond, marginally ahead of Claude Opus 4.7 (94.2%).
Its agentic tool use improvement is notable: Google reports 82% improvement in agentic tool use versus its predecessor. MCP-Atlas scores it at 73.9%, placing it between Claude Opus 4.7’s 77.3% and GPT-5.4’s 68.1%. For web research specifically, it scores 85.9% on BrowseComp, which tests the ability to find obscure information through iterative web browsing.
The 1M-token context window (1,048,576 tokens precisely) supports full codebase ingestion in a single session, with 64K output tokens. Multimodal input covers text, images, video, audio, and code in a single native model.
Pricing is the clearest advantage: $2.00 input and $12.00 output per million tokens for requests under 200K tokens. For requests that cross 200K tokens, the entire request bills at $4.00 input and $18.00 output per million. At standard sizes, Gemini 3.1 Pro costs roughly 60% less than Claude Opus 4.7 output and 60% less than GPT-5.5 output.
As of the time of writing, Gemini 3.1 Pro remains in preview. General availability confirmation is pending performance validation from the preview period.
Feature-by-Feature Breakdown
Agentic Coding Performance
The clearest split is between terminal-first agents and codebase-first agents.
GPT-5.5 is built for terminal-first scenarios. Its 82.7% Terminal-Bench 2.0 score reflects the model’s ability to plan across CLI steps, iterate on errors, and coordinate tools in long autonomous loops. Senior engineers at Cursor and NVIDIA, quoted in OpenAI’s release notes, reported that GPT-5.5 identifies where fixes belong and predicts downstream impacts across a codebase better than GPT-5.4.
Claude Opus 4.7 is better suited for codebase-first scenarios. Its 64.3% on SWE-bench Pro, the harder variant requiring understanding of real GitHub repositories across multiple files and languages, shows it handles ambiguity and system-level reasoning more reliably. Analysis from MindStudio notes that Opus 4.7 is the stronger choice when a model must read a real codebase, reason through unclear requirements, and avoid shallow fixes.
Gemini 3.1 Pro’s 54.2% SWE-bench Pro score puts it third on multi-file coding, though its strong BrowseComp (85.9%) and ARC-AGI-2 (77.1%) numbers make it genuinely useful for agents that combine web research with coding tasks.
Tool Use and MCP Compatibility
MCP-Atlas is currently the best available benchmark for multi-turn tool orchestration, and Claude Opus 4.7 leads at 77.3%, ahead of Gemini 3.1 Pro at 73.9% and GPT-5.4 at 68.1%. GPT-5.5 scores were not published separately from GPT-5.4 at the time of writing, but the agentic improvements should push that number up.
Opus 4.7’s practical advantage in tool use comes partly from Anthropic’s long-standing investment in the Model Context Protocol. The model’s self-verification behavior, which checks outputs before reporting back, reduces hallucinated tool results in extended pipelines.
Context Window Usage
All three models nominally support 1M token context. In practice, GPT-5.5 drops to 400K inside Codex. Requests above 272K tokens also trigger a pricing step-up for GPT-5.5, which affects the economics of loading large codebases.
Gemini 3.1 Pro’s 1,048,576-token context with 64K output is cleanly designed for codebase-scale sessions. Claude Opus 4.7 maintains 1M input with strong long-context coherence, which Anthropic claims is engineered to sustain focus over hours-long agentic workflows.
Multimodal Capabilities
GPT-5.5 introduced a unified architecture where text, images, audio, and video are processed in a single pass rather than through stitched-together subsystems. This reduces artifacts in mixed-media reasoning and matters for agents that need to act on screen content or audio alongside code.
Claude Opus 4.7 improved image resolution support to 2,576 pixels on the long edge, about 3.75 megapixels. For agents navigating web UIs via screenshots or reading high-resolution technical diagrams, this is a concrete upgrade.
Gemini 3.1 Pro supports the same modalities natively and adds video comprehension, which the other two models handle less cleanly for long-form video context.
Speed and Token Efficiency
GPT-5.5 latency analysis shows it matches GPT-5.4 per-token latency in real-world serving while performing at a higher intelligence level. It also uses roughly 72% fewer output tokens on equivalent coding tasks versus Claude Opus 4.7, which at high volume offsets the higher per-token output price.
For interactive development, developers report GPT-5.5 feels faster and snappier. Claude Opus 4.7 can be verbose before reaching code. For fully automated overnight pipelines where latency is less important than accuracy, this distinction matters less.
Pricing in Context
At standard API pricing for a typical 5K input / 2K output token request:
- GPT-5.5: approximately $0.085 per 1,000 requests
- Claude Opus 4.7: approximately $0.075 per 1,000 requests
- Gemini 3.1 Pro: approximately $0.034 per 1,000 requests (under 200K tokens)
Gemini 3.1 Pro is substantially cheaper at scale. For teams running millions of agentic calls per month, the difference is meaningful. The tradeoff is lower SWE-bench Pro performance and still-in-preview status.
Reasoning on Novel Problems
Gemini 3.1 Pro’s 77.1% ARC-AGI-2 score stands out. ARC-AGI-2 specifically tests tasks the model cannot have memorized from training, requiring genuine compositional reasoning. For agentic workflows that require working through genuinely new problem types, this is the most encouraging signal from Gemini 3.1 Pro.
GPQA Diamond scores, which test graduate-level science reasoning, are essentially tied: GPT-5.5 (94.4% via GPT-5.4 Pro data), Claude Opus 4.7 (94.2%), and Gemini 3.1 Pro (94.3%).
Who Should Use Which Model?
Use GPT-5.5 if:
- You are building terminal-based agents that operate through CLI tools, shell scripts, or autonomous technical loops.
- Your agent needs to operate computer environments directly, using mouse and keyboard actions, without a human in the loop.
- You need high-volume output and token efficiency matters more than top-percentile code accuracy.
- You want a model that integrates cleanly with Codex and OpenAI’s Operator infrastructure.
- Your users are on ChatGPT Plus, Pro, Business, or Enterprise tiers and you want a unified consumer plus API experience.
Use Claude Opus 4.7 if:
- Your agent needs to read and modify real, large codebases across multiple files and languages.
- You rely heavily on MCP-connected tools and need reliable multi-turn tool orchestration.
- Correctness on complex, ambiguous engineering tasks matters more than raw speed.
- You need fine-grained control over reasoning effort, including the new “xhigh” budget tier.
- You want task budgets that limit how many tokens individual subtasks can consume.
Use Gemini 3.1 Pro if:
- Cost is a primary constraint and you are running high-volume production pipelines.
- Your agent combines web research with other tasks and BrowseComp-style information retrieval is important.
- You need strong novel reasoning for genuinely new problem types, not standard coding patterns.
- You need video comprehension as part of an agentic loop.
- You are already inside the Google Cloud ecosystem and want Vertex AI integration.
Verdict
There is no single winner here, and framing it as a horse race misses the point. These three models are genuinely differentiated in ways that matter for different kinds of agentic work.
GPT-5.5 is the strongest model for terminal-based autonomous agents. Its 82.7% Terminal-Bench 2.0 score and 78.7% OSWorld-Verified score are not close calls. If you are building agents that execute commands, navigate computer interfaces, and run long autonomous loops with minimal human review, GPT-5.5 is the current leader.
Claude Opus 4.7 is the strongest model for complex multi-file software engineering. SWE-bench Pro is the best available proxy for real GitHub issue resolution, and Opus 4.7 leads at 64.3%. Its MCP-Atlas lead (77.3%) also makes it the safest choice for tool-heavy pipelines where incorrect tool calls have expensive downstream consequences. If your agent is doing code review, refactoring, or working through ambiguous engineering requirements, Opus 4.7 is the better bet.
Gemini 3.1 Pro is the strongest model for cost-sensitive pipelines and novel reasoning tasks. Its 77.1% ARC-AGI-2 score is a genuine achievement and suggests the model performs better on truly new problem types than the competition. At roughly 60% lower output cost than the other two, it makes production-scale agentic deployments financially viable in ways that GPT-5.5 and Opus 4.7 are not, assuming you can accept its current preview status and lower SWE-bench Pro numbers.
For most teams building in 2026, the practical answer is to route tasks to different models based on their nature: GPT-5.5 for computer-use and terminal work, Claude Opus 4.7 for code understanding and tool orchestration, and Gemini 3.1 Pro for research-heavy or cost-sensitive subtasks.
FAQ
Is GPT-5.5 better than Claude Opus 4.7 overall?
Neither is better overall. GPT-5.5 leads on terminal-based agentic tasks (Terminal-Bench 2.0: 82.7% vs 69.4%) and is more token-efficient. Claude Opus 4.7 leads on real-world coding benchmarks (SWE-bench Pro: 64.3% vs 58.6%) and multi-turn tool orchestration (MCP-Atlas: 77.3%). Which is “better” depends entirely on what you are building.
Is Gemini 3.1 Pro as good as GPT-5.5 for coding?
On pure coding benchmarks like SWE-bench Pro, Gemini 3.1 Pro (54.2%) falls short of both GPT-5.5 (58.6%) and Claude Opus 4.7 (64.3%). Where Gemini 3.1 Pro stands out is novel reasoning (ARC-AGI-2: 77.1%), competitive programming (LiveCodeBench: 2887 Elo), and web research tasks. For standard software engineering agentic work, the other two models currently outperform it.
What does “agentic workflow” mean in the context of these models?
An agentic workflow is one where the model operates over multiple steps autonomously, using tools, writing and executing code, browsing the web, or interacting with software, without a human approving each action. The benchmarks most relevant here are Terminal-Bench 2.0 (CLI autonomy), SWE-bench Pro (multi-file code changes), MCP-Atlas (tool orchestration), and OSWorld-Verified (computer use).
How much does it cost to run 1 million API calls with each model?
At a typical 5K input / 2K output token request: Gemini 3.1 Pro costs roughly $34 per 1,000 requests (under 200K token context), Claude Opus 4.7 costs roughly $75, and GPT-5.5 costs roughly $85. At 1 million calls, Gemini 3.1 Pro saves around $41,000 to $51,000 versus the other two models at these estimates.
Can these models run multi-day agentic tasks without losing coherence?
Anthropic explicitly engineered Opus 4.7 for sustained focus on hours-long workflows and added self-verification before reporting outputs. GPT-5.5 similarly targets long-running pipelines with its Codex integration. Gemini 3.1 Pro’s improved long-horizon stability is mentioned in Google’s release materials. None of the three has published independent data specifically on multi-day coherence, so user testing for specific pipelines remains necessary.
Which model is best for agents that use MCP tools?
Claude Opus 4.7 leads MCP-Atlas at 77.3%, ahead of Gemini 3.1 Pro (73.9%). This benchmark measures complex multi-turn tool-calling scenarios and is currently the best available proxy for real MCP tool orchestration performance. Anthropic’s ongoing investment in MCP as an open protocol also means Opus 4.7 tends to receive MCP-related updates earlier than competing models.
Is Gemini 3.1 Pro available to everyone right now?
As of the time of writing, Gemini 3.1 Pro is in preview via Google AI Studio, the Gemini API, and Vertex AI. Google confirmed it will move to general availability after the preview period validates performance. Teams can use it in production today through the preview API, but should account for the possibility of behavioral changes before GA.
Does the Claude Opus 4.7 tokenizer change affect my existing prompts?
Yes, it can. Anthropic’s updated tokenizer for Opus 4.7 maps the same input text to roughly 1.0 to 1.35 times more tokens than Opus 4.6, depending on content type. If you are migrating existing pipelines from Opus 4.6 to 4.7, you should test your actual prompts and measure token counts before assuming pricing is equivalent. Anthropic’s third-party cost analysis covers this in detail.
Which model has the best multimodal support for agentic screen navigation?
For screen-based navigation in computer-use agents, GPT-5.5 and Claude Opus 4.7 are the strongest options. GPT-5.5’s unified architecture processes visual and text context in a single pass, and it scores 78.7% on OSWorld-Verified. Claude Opus 4.7’s improved image resolution (up to 3.75 megapixels) helps with reading high-resolution UI screenshots. Gemini 3.1 Pro has not published OSWorld-Verified scores but leads on video understanding for longer-form visual context.
