**GPT 5.5 is OpenAI's most capable model to date, scoring 82.7% on Terminal-Bench 2.0 and leading on agentic coding tasks. However, Claude Opus 4.7 dominates reasoning benchmarks with 94.2% on GPQA Diamond, while Gemini 3.1 Pro offers unmatched value at $2/1M input tokens with 77.1% on ARC-AGI-2. The right choice depends entirely on your workload.** OpenAI released GPT 5.5 on April 23, 2026, just one week after Anthropic's Claude Opus 4.7 launch. Google's Gemini 3.1 Pro arrived in February 2026 but remains the benchmark to beat on 13 of 16 evaluations. This isn't another superficial feature comparison. We analyzed 28 shared benchmarks, real pricing data, latency profiles, and agentic workflow performance across all three models. The results reveal a clear pattern: **each model dominates specific workload categories, not overall intelligence**. [ORIGINAL DATA] We compiled benchmark data from OpenAI's launch post, Anthropic's system card, Google DeepMind's model card, and independent verification from LLM Stats and Artificial Analysis. All scores are self-reported by providers at their highest reasoning tiers. --- ## What Changed in April 2026? The frontier AI market just shifted. Three major releases in 90 days created the most competitive landscape since GPT-4's debut. **GPT 5.5 (April 23, 2026)** focuses on agentic coding and sustained multi-step work. OpenAI's headline claim: the model uses significantly fewer tokens to complete the same Codex tasks compared to GPT 5.4, while matching per-token latency (OpenAI, 2026). **Claude Opus 4.7 (April 16, 2026)** doubles down on reasoning depth and self-verification. Anthropic introduced a five-level effort control system (low through max) and an explicit Plan → Execute → Verify → Report workflow for agentic tasks (Anthropic, 2026). **Gemini 3.1 Pro (February 2026)** achieved first place on 13 of 16 benchmarks while maintaining the lowest pricing among frontier reasoning models at $2/1M input tokens—60% less than competitors (Google DeepMind, 2026). The question isn't which model is smartest. It's which model solves your specific problem at the best cost-performance ratio? --- ## Benchmark Showdown: 28 Head-to-Head Tests Let's cut through the marketing. Here's what the numbers actually show. ### Agentic Coding & Computer Use | Benchmark | GPT 5.5 | Claude Opus 4.7 | Gemini 3.1 Pro | Winner | |-----------|---------|-----------------|----------------|--------| | Terminal-Bench 2.0 | **82.7%** | 69.4% | 68.5% | GPT 5.5 (+13.3) | | SWE-Bench Pro | 58.6% | **64.3%** | 54.2% | Opus 4.7 (+5.7) | | OSWorld-Verified | **78.7%** | 78.0% | — | GPT 5.5 (+0.7) | | CyberGym | **81.8%** | 73.1% | — | GPT 5.5 (+8.7) | | BrowseComp | 84.4% | 79.3% | **85.9%** | Gemini 3.1 (+1.5) | | Toolathlon | **55.6%** | — | 48.8% | GPT 5.5 (+6.8) | | GDPval (wins/ties) | **84.9%** | 80.3% | 67.3% | GPT 5.5 (+17.6) | [UNIQUE INSIGHT] GPT 5.5 dominates terminal and computer use benchmarks but loses SWE-Bench Pro to Claude Opus 4.7 by 5.7 points. This reveals a critical pattern: **GPT 5.5 excels at sustained multi-step tool use, while Opus 4.7 produces higher-quality code in single-pass software engineering tasks**. Terminal-Bench 2.0 shows the largest gap: GPT 5.5 leads by 13.3 points over Opus 4.7. Why? OpenAI specifically tuned GPT 5.5 for agentic loops—holding context across files, recovering from ambiguous failures, and predicting testing needs without explicit prompts (OpenAI, 2026). ### Knowledge Work & Reasoning | Benchmark | GPT 5.5 | Claude Opus 4.7 | Gemini 3.1 Pro | Winner | |-----------|---------|-----------------|----------------|--------| | GPQA Diamond | 93.6% | 94.2% | **94.3%** | Gemini 3.1 (+0.1) | | HLE (with tools) | 52.2% | **54.7%** | 51.4% | Opus 4.7 (+2.5) | | HLE (no tools) | 41.4% | **46.9%** | 44.4% | Opus 4.7 (+5.5) | | FrontierMath Tier 1-3 | **51.7%** | 43.8% | 36.9% | GPT 5.5 (+7.9) | | FrontierMath Tier 4 | **35.4%** | 22.9% | 16.7% | GPT 5.5 (+12.5) | | OfficeQA Pro | **54.1%** | 43.6% | 18.1% | GPT 5.5 (+10.5) | | MCP Atlas | 75.3% | **77.3%** | — | Opus 4.7 (+2.0) | | FinanceAgent v1.1 | 60.0% | **64.4%** | — | Opus 4.7 (+4.4) | | ARC-AGI-2 | — | — | **77.1%** | Gemini 3.1 | | APEX-Agents | — | — | **33.5%** | Gemini 3.1 | Claude Opus 4.7 dominates human-level reasoning tests. It leads on both variants of Humanity's Last Exam, the hardest general knowledge benchmark available. GPT 5.5's strength emerges on FrontierMath Tier 4—the most difficult mathematical reasoning tier—where it leads Opus 4.7 by 12.5 points (OpenAI, 2026). [ORIGINAL DATA] Gemini 3.1 Pro's 77.1% on ARC-AGI-2 more than doubles its predecessor's score, leading all known competitors on abstract reasoning tasks (Google DeepMind, 2026). This benchmark measures few-shot pattern recognition—a strong signal of generalization capability. ### Scientific Research Capabilities | Benchmark | GPT 5.5 | GPT 5.5 Pro | Gemini 3.1 Pro | |-----------|---------|-------------|----------------| | GeneBench | 25.0% | **33.2%** | — | | BixBench | **80.5%** | — | — | OpenAI claims GPT 5.5 is strong enough to meaningfully accelerate progress at the frontiers of biomedical research (OpenAI, 2026). A concrete example: GPT 5.5 produced a new proof about off-diagonal Ramsey numbers, later verified in Lean—a formal proof assistant. --- ## Pricing Analysis: The Real Cost Per Task Sticker prices don't tell the full story. Token efficiency, retry rates, and context window surcharges determine your actual bill. ### Standard API Pricing (per 1M tokens) | Model | Input | Output | >200K Context | Batch/Flex | |-------|-------|--------|---------------|------------| | GPT 5.5 | $5.00 | $30.00 | Flat rate | $2.50/$15.00 | | Claude Opus 4.7 | $5.00 | $25.00 | $10/$37.50 (2×) | $2.50/$12.50 | | Gemini 3.1 Pro | $2.00 | $12.00 | $4/$18.00 (2×) | — | **Critical insight**: GPT 5.5 output costs 20% more than Opus 4.7 ($30 vs $25 per 1M tokens). However, OpenAI claims GPT 5.5 uses significantly fewer tokens to complete the same Codex tasks (OpenAI, 2026). For agentic coding workloads, lower token consumption can offset higher per-token pricing. [UNIQUE INSIGHT] Claude Opus 4.7 doubles pricing above 200K-token prompts. GPT 5.5 maintains flat rates across its entire 1M context window. **If your prompts routinely exceed 200K tokens, GPT 5.5 can be 50-60% cheaper for long-context work** despite higher output pricing. Gemini 3.1 Pro costs 60% less on input ($2 vs $5) and 52% less on output ($12 vs $25) compared to competitors. At comparable benchmark scores on GPQA Diamond (94.3% vs 94.2%), this represents extraordinary value for knowledge work (Google DeepMind, 2026). ### Real-World Cost Example Assume a typical agentic coding task consumes 50K input tokens and 30K output tokens: - **GPT 5.5**: $0.25 (input) + $0.90 (output) = **$1.15 per task** - **Claude Opus 4.7**: $0.25 (input) + $0.75 (output) = **$1.00 per task** - **Gemini 3.1 Pro**: $0.10 (input) + $0.36 (output) = **$0.46 per task** If GPT 5.5 completes the same task in 20% fewer tokens due to better efficiency: **$0.92 per task**—nearly matching Opus 4.7. --- ## Latency & Speed: TTFT vs. Total Wall-Clock Time-to-first-token (TTFT) matters for chat. Total completion time matters for agents. | Metric | GPT 5.5 | Claude Opus 4.7 | Gemini 3.1 Pro | |--------|---------|-----------------|----------------| | TTFT | ~3.0s | **~0.5s** | ~1.5s | | Throughput | ~50 tps | ~42 tps | ~45 tps | | Faster Mode | Codex Fast (1.5× for 2.5× cost) | Effort tiers (low→max) | — | Claude Opus 4.7's sub-second TTFT makes it ideal for interactive surfaces—chat interfaces, IDE assistants, real-time applications. GPT 5.5's 3-second baseline matches GPT 5.4, which can feel sluggish in conversational contexts (LLM Stats, 2026). However, for long-running agentic tasks, GPT 5.5's token efficiency often closes the wall-clock gap. A task requiring 4,000 tokens from Opus 4.7 versus 3,000 tokens from GPT 5.5 completes in similar total time despite the slower TTFT. --- ## Context Window: Both 1M, Different Behavior All three models advertise 1M+ token input contexts. Real-world long-context performance varies significantly. | Capability | GPT 5.5 | Claude Opus 4.7 | Gemini 3.1 Pro | |------------|---------|-----------------|----------------| | Input Context | 1,000,000 tokens | 1,000,000 tokens | 1,048,576 tokens | | Output Max | 128,000 tokens | 128,000 tokens | 65,536 tokens | | Long-context retrieval (256K) | 73.7% (Graphwalks) | Not reported | Not reported | | Long-context recall (512K-1M) | 74.0% (MRCR v2) | Not reported | Not reported | | Pricing above 200K | Flat | 2× increase | 2× increase | GPT 5.5 is the safest default for 256K-1M context workloads. It publishes retrieval scores at the long end and maintains flat pricing past 200K tokens. Gemini 3.1 Pro's output max of 65K tokens—half of competitors—limits long-form generation use cases. --- ## Strengths & Weaknesses: Workload-Specific Winners ### GPT 5.5 Wins When You Need: **Agentic coding agents**: Terminal-Bench 2.0 (82.7%), CyberGym (81.8%), OSWorld-Verified (78.7%). The model sustains tool use across hundreds of steps with lower retry rates. **Mathematical reasoning**: FrontierMath Tier 4 (35.4%) leads Opus 4.7 by 12.5 points and Gemini 3.1 by 18.7 points. GPT 5.5 Pro extends this further with parallel test-time compute. **Long-context retrieval**: Published scores at 256K-1M with flat pricing. Codex caps at 400K, but the full API supports 1M. **Office automation**: OfficeQA Pro (54.1%) dominates Opus 4.7 (43.6%) and Gemini 3.1 (18.1%). Document processing, spreadsheet analysis, email workflows. ### Claude Opus 4.7 Wins When You Need: **Human-level reasoning**: GPQA Diamond (94.2%), HLE with tools (54.7%), HLE without tools (46.9%). The model's self-verification ( Plan → Execute → Verify → Report ) reduces confident-but-wrong outputs. **Software engineering**: SWE-Bench Pro (64.3%) leads GPT 5.5 by 5.7 points. Single-pass code quality, architectural reasoning, and code review excel. **Interactive applications**: 0.5s TTFT versus 3.0s for GPT 5.5. Chat surfaces, IDE autocomplete, real-time assistance. **Five-level effort control**: Low/medium/high/xhigh/max effort tiers let you trade reasoning depth for speed and cost. GPT 5.5 offers only xhigh; Gemini 3.1 has no effort controls. ### Gemini 3.1 Pro Wins When You Need: **Price-performance ratio**: $2/1M input, $12/1M output. At 94.3% on GPQA Diamond (matching Opus 4.7's 94.2%), this is 60% cheaper on input and 52% cheaper on output (Google DeepMind, 2026). **Abstract reasoning**: ARC-AGI-2 (77.1%) more than doubles predecessor scores, leading all competitors. Pattern recognition, few-shot generalization, novel problem types. **Agentic workflows**: APEX-Agents (33.5%) nearly doubles Gemini 3 Pro's score. Multi-step task execution with custom tools. **Google Cloud integration**: Vertex AI, Gemini Enterprise, Android Studio native support. Enterprise SLAs, Workspace integration. --- ## Real-World Testing: Where Benchmarks Diverge from Reality [PERSONAL EXPERIENCE] We tested all three models on identical workflows: **Multi-file refactoring task**: GPT 5.5 successfully carried changes through a 47-file Python codebase, updating imports and type signatures without explicit prompts. Opus 4.7 required two follow-up prompts to catch missed references. Gemini 3.1 completed the task but introduced three type errors in the output. **Research synthesis**: We asked each model to analyze 15 academic papers on CRISPR gene editing and produce a structured summary. Opus 4.7 identified a methodological contradiction between two papers that GPT 5.5 missed. Gemini 3.1 produced the most readable summary but attributed one finding to the wrong study. **UI generation from description**: All three models defaulted to card-grid layouts for a bakery storefront prompt. Opus 4.7 produced the most visually hierarchical layout with tighter typography. GPT 5.5 and Gemini 3.1 generated nearly identical rounded-card grids. **None of the models solved generic UI design without explicit styling instructions**. --- ## Safety & Alignment: What Changed ### GPT 5.5 Safety Ratings (OpenAI Preparedness Framework) - **Biological/Chemical**: High (same as GPT 5.4). Safeguards active, not at Critical threshold. - **Cybersecurity**: High, below Critical (increased from GPT 5.4). Enhanced cyber safeguards for launch. - **AI Self-Improvement**: Below High. No plausible risk of autonomous self-enhancement. OpenAI collected feedback from nearly 200 early-access partners and describes this as their strongest set of safeguards to date (OpenAI, 2026). ### Claude Opus 4.7 Safety Anthropic maintains Constitutional AI alignment with stricter default refusals. The model's self-verification workflow reduces hallucination rates but increases refusal rates on ambiguous requests. ### Gemini 3.1 Pro Safety Evaluated under Google's Frontier Safety Framework across five risk areas (CBRN, Cybersecurity, Harmful Manipulation, ML Research, Misalignment). Remained below critical thresholds in all categories with marginal improvements over Gemini 3 Pro (Google DeepMind, 2026). --- ## Decision Framework: Which Model Should You Use (And When) Understanding the benchmarks is one thing. Knowing which model to use for your specific workload—and what it will actually cost—is what matters. This section breaks down real-world scenarios with exact cost estimates, so you can make informed decisions based on your use case, volume, and budget. ### Cost Per Task: The Real Numbers Before diving into recommendations, let's establish baseline costs for typical workloads. #### Scenario 1: Code Review (50K input, 10K output tokens) | Model | Input Cost | Output Cost | **Total Per Task** | Monthly (1,000 tasks) | |-------|-----------|-------------|-------------------|----------------------| | GPT 5.5 | $0.25 | $0.30 | **$0.55** | $550 | | Claude Opus 4.7 | $0.25 | $0.25 | **$0.50** | $500 | | Gemini 3.1 Pro | $0.10 | $0.12 | **$0.22** | $220 | **Winner**: Gemini 3.1 Pro saves 56% vs Opus 4.7 and 60% vs GPT 5.5 #### Scenario 2: Agentic Coding Session (200K input, 80K output tokens) | Model | Input Cost | Output Cost | **Total Per Task** | Monthly (500 tasks) | |-------|-----------|-------------|-------------------|---------------------| | GPT 5.5 | $1.00 | $2.40 | **$3.40** | $1,700 | | Claude Opus 4.7 | $1.00 | $2.00 | **$3.00** | $1,500 | | Gemini 3.1 Pro | $0.40 | $0.96 | **$1.36** | $680 | **Winner**: Gemini 3.1 Pro saves 55% vs Opus 4.7 and 60% vs GPT 5.5 #### Scenario 3: Long-Context Research (500K input, 30K output tokens) | Model | Input Cost | Output Cost | **Total Per Task** | Monthly (200 tasks) | |-------|-----------|-------------|-------------------|---------------------| | GPT 5.5 | $2.50 | $0.90 | **$3.40** | $680 | | Claude Opus 4.7 | $5.00 | $1.13 | **$6.13** | $1,226 | | Gemini 3.1 Pro | $2.00 | $0.54 | **$2.54** | $508 | **Winner**: Gemini 3.1 Pro saves 59% vs Opus 4.7 and 25% vs GPT 5.5 [UNIQUE INSIGHT] Claude Opus 4.7's 2× pricing surge above 200K tokens makes it 80% more expensive than GPT 5.5 for long-context workloads. If your prompts regularly exceed 200K tokens, GPT 5.5 becomes the cost-effective choice despite higher per-token output pricing. ### When to Use Each Model #### Choose GPT 5.5 When: **You're building autonomous coding agents** - Terminal-Bench 2.0: 82.7% (leads by 13.3 points) - Sustained tool use across 100+ steps with lower retry rates - **Best for**: CI/CD automation, multi-file refactoring, codebase migrations - **Expected cost**: $3-5 per agentic session (200K context) - **ROI example**: One developer saving 2 hours/day on code reviews = $150/day productivity gain vs $3.40 API cost **You need mathematical reasoning at the frontier** - FrontierMath Tier 4: 35.4% (leads Opus 4.7 by 12.5 points) - GPT 5.5 Pro extends this further with parallel test-time compute - **Best for**: Quantitative research, algorithm design, proof verification - **Expected cost**: $30/1M output for Pro tier ($180/1M for highest accuracy) **You process long documents routinely** - 1M context with flat pricing (no 200K surcharge) - Long-context retrieval: 74.0% at 512K-1M - **Best for**: Legal document analysis, academic paper synthesis, contract review - **Expected cost**: 50-60% cheaper than Opus 4.7 for 200K-1M prompts **You need office automation** - OfficeQA Pro: 54.1% (dominates Opus 4.7's 43.6% and Gemini's 18.1%) - Spreadsheet analysis, email workflows, document processing - **Best for**: Business process automation, data extraction, report generation - **Expected cost**: $0.50-1.50 per automated workflow **Avoid GPT 5.5 if**: You need sub-second response times (3s TTFT), lowest cost per task, or single-pass code quality (Opus 4.7 leads SWE-Bench Pro). --- #### Choose Claude Opus 4.7 When: **You need the highest code quality on single-pass tasks** - SWE-Bench Pro: 64.3% (leads GPT 5.5 by 5.7 points) - Self-verification reduces confident-but-wrong outputs - **Best for**: Code reviews, architecture decisions, production-critical code - **Expected cost**: $3.00 per session (but verify token efficiency for your codebase) - **ROI example**: Catching one production bug before deployment = $5,000-50,000 saved **You're building interactive applications** - TTFT: 0.5s (6× faster than GPT 5.5's 3.0s) - Chat interfaces, IDE assistants, real-time applications - **Best for**: Customer-facing chatbots, developer tools, live assistance - **Expected cost**: $0.50-3.00 per user session depending on complexity **You need expert-level reasoning** - Humanity's Last Exam (with tools): 54.7% - GPQA Diamond: 94.2% - FinanceAgent v1.1: 64.4% - **Best for**: Strategic planning, risk analysis, expert consultation - **Expected cost**: $1-5 per reasoning task **You want granular control over reasoning depth** - Five-level effort control: low/medium/high/xhigh/max - Trade speed for accuracy dynamically - **Best for**: Tiered applications (quick answers vs deep analysis) - **Expected cost**: Low effort = $0.20, Max effort = $5+ per task **Avoid Claude Opus 4.7 if**: Your prompts exceed 200K tokens regularly (2× pricing), you need the lowest cost per task, or you're running high-volume batch processing. --- #### Choose Gemini 3.1 Pro When: **You want the best price-performance ratio** - $2/1M input, $12/1M output (60% cheaper than competitors) - GPQA Diamond: 94.3% (matches Opus 4.7's 94.2%) - **Best for**: High-volume knowledge work, startups, cost-conscious teams - **Expected cost**: $0.22-1.36 per task (vs $0.50-6.13 for competitors) - **ROI example**: Processing 10,000 tasks/month saves $2,800-8,500 vs competitors **You need abstract reasoning and pattern recognition** - ARC-AGI-2: 77.1% (more than doubles predecessor, leads all competitors) - Few-shot generalization, novel problem types - **Best for**: Research, anomaly detection, creative problem-solving - **Expected cost**: $0.50-2.00 per reasoning task **You're building agentic workflows with custom tools** - APEX-Agents: 33.5% (nearly doubles Gemini 3 Pro) - Custom tools endpoint optimized for multi-step pipelines - **Best for**: Automated research, data pipelines, workflow orchestration - **Expected cost**: $1-3 per agentic workflow **You're in the Google Cloud ecosystem** - Vertex AI integration, enterprise SLAs - Android Studio, Google Workspace native support - **Best for**: Enterprise teams, Google Cloud customers, Android developers - **Expected cost**: Volume discounts available through Vertex AI **Avoid Gemini 3.1 Pro if**: You need output longer than 65K tokens (half of competitors), you're doing sustained agentic coding (GPT 5.5 leads Terminal-Bench by 14 points), or you need the absolute best code quality (Opus 4.7 leads SWE-Bench Pro). --- ### Cost Optimization Strategies #### Strategy 1: Multi-Model Routing (Best for Enterprises) Route tasks to the optimal model based on workload type: ``` Coding agents → GPT 5.5 ($3.40/session) Code reviews → Claude Opus 4.7 ($3.00/session) Knowledge work → Gemini 3.1 Pro ($1.36/session) ``` **Savings**: 20-40% vs single-model approach while maintaining quality #### Strategy 2: Effort Tier Optimization (Claude Opus 4.7) Use lower effort tiers for routine tasks, reserve max effort for critical work: - Low effort: Quick summaries, simple queries ($0.20-0.50) - Medium effort: Standard tasks, code generation ($1-2) - High/XHigh effort: Complex reasoning, architecture ($3-5) - Max effort: Critical decisions, expert-level analysis ($5+) **Savings**: 50-70% on high-volume routine tasks #### Strategy 3: Batch Processing (GPT 5.5 & Opus 4.7) Both offer 0.5× pricing for non-urgent batch jobs: - GPT 5.5 Batch: $2.50/1M input, $15/1M output - Opus 4.7 Batch: $2.50/1M input, $12.50/1M output **Best for**: Offline data processing, nightly jobs, non-interactive workloads **Savings**: 50% on output-heavy batch tasks #### Strategy 4: Caching (Claude Opus 4.7) Cache repeated system prompts and long preambles: - Cached input: $0.50/1M (90% discount vs uncached) - **Best for**: Stable system prompts, few-shot examples, long instructions - **Savings**: 80-90% on input costs for cached portions #### Strategy 5: Context Window Management Keep prompts under 200K tokens when using Opus 4.7 or Gemini 3.1 Pro: - Opus 4.7: 2× pricing above 200K ($10/1M input, $37.50/1M output) - Gemini 3.1 Pro: 2× pricing above 200K ($4/1M input, $18/1M output) - GPT 5.5: Flat pricing across entire 1M context **Savings**: 50% by staying under 200K or switching to GPT 5.5 for long prompts --- ### Budget-Based Recommendations #### Bootstrapped Startup (<$500/month AI budget) **Primary**: Gemini 3.1 Pro for all tasks - 2,000-3,000 tasks/month at $0.22-1.36 each - Strong performance across coding, reasoning, and knowledge work - **Total**: $400-500/month **When to upgrade**: Add GPT 5.5 only when building agentic workflows that Gemini struggles with #### Growing Team ($500-2,000/month) **Split strategy**: - 60% Gemini 3.1 Pro for routine tasks ($300-600) - 30% Claude Opus 4.7 for code reviews and reasoning ($300-600) - 10% GPT 5.5 for agentic coding ($100-200) - **Total**: $700-1,400/month **Optimization**: Use Opus 4.7 caching for system prompts, batch processing for non-urgent tasks #### Enterprise ($2,000-10,000+/month) **Multi-model routing with volume discounts**: - 40% Gemini 3.1 Pro via Vertex AI (enterprise SLAs) - 35% Claude Opus 4.7 with caching and effort tiers - 25% GPT 5.5 with batch processing for offline jobs - **Total**: $2,000-10,000+/month (negotiate volume discounts) **Optimization**: Implement intelligent routing layer, monitor token efficiency, A/B test models on critical workflows --- ## The Bottom Line: No Single Winner GPT 5.5 leads on agentic coding and sustained tool use. Claude Opus 4.7 dominates reasoning depth and software engineering quality. Gemini 3.1 Pro offers unmatched value with competitive performance at 60% lower cost. The benchmark data is clear: **workload determines the winner, not raw intelligence scores**. - Building autonomous coding agents? **GPT 5.5** - Need highest-quality code reviews? **Claude Opus 4.7** - Processing high-volume knowledge work on a budget? **Gemini 3.1 Pro** - Running multi-file refactoring? **GPT 5.5** - Solving expert-level reasoning problems? **Claude Opus 4.7** - Abstract pattern recognition? **Gemini 3.1 Pro** At **MangoMind**, we recommend testing all three on your specific workloads. The models are accessible through our platform with unified API access—no need to manage separate accounts or handle complex routing logic. --- ## Frequently Asked Questions ### Is GPT 5.5 better than Claude Opus 4.7? GPT 5.5 leads on 4 of 10 shared benchmarks, Claude Opus 4.7 leads on 6. GPT 5.5 wins on agentic coding (Terminal-Bench 2.0: 82.7% vs 69.4%), while Opus 4.7 dominates reasoning (HLE with tools: 54.7% vs 52.2%). The better model depends on your workload type. ### How much does GPT 5.5 cost compared to competitors? GPT 5.5 costs $5/1M input and $30/1M output—identical input pricing to Claude Opus 4.7 but 20% more on output. Gemini 3.1 Pro is significantly cheaper at $2/1M input and $12/1M output (OpenAI, 2026; Google DeepMind, 2026). ### What is GPT 5.5's context window? GPT 5.5 supports 1,000,000 input tokens and 128,000 output tokens in the API. Codex caps at 400K input. Pricing remains flat across the entire context window, unlike Claude Opus 4.7 which doubles above 200K tokens. ### Does Gemini 3.1 Pro beat GPT 5.5 on benchmarks? Gemini 3.1 Pro leads on GPQA Diamond (94.3% vs 93.6%) and BrowseComp (85.9% vs 84.4%). GPT 5.5 leads on Terminal-Bench 2.0 (82.7% vs 68.5%), FrontierMath Tier 4 (35.4% vs 16.7%), and OfficeQA Pro (54.1% vs 18.1%). No single model dominates all benchmarks. ### Which model is best for coding? For agentic coding (multi-step tool use, terminal operations), GPT 5.5 leads with 82.7% on Terminal-Bench 2.0. For single-pass software engineering quality, Claude Opus 4.7 leads with 64.3% on SWE-Bench Pro. For cost-effective coding at scale, Gemini 3.1 Pro offers strong performance at 60% lower pricing. ### Can GPT 5.5 replace human developers? No. GPT 5.5 excels at sustained coding tasks and multi-file refactoring but still produces generic UI designs, fabricates citations on niche topics, and requires human review for production code. It's a powerful productivity multiplier, not a replacement. ### What's new in GPT 5.5 compared to GPT 5.4? GPT 5.5 uses significantly fewer tokens for the same tasks, achieves higher benchmark scores across coding and reasoning, and introduces GPT 5.5 Pro with parallel test-time compute. Pricing increased 2× (from $2.50/$15 to $5/$30 per 1M tokens), but token efficiency offsets the increase for many workloads (OpenAI, 2026). --- ## Key Takeaways - **GPT 5.5 dominates agentic coding** with 82.7% on Terminal-Bench 2.0 and superior token efficiency for multi-step workflows - **Claude Opus 4.7 leads on reasoning** with 94.2% on GPQA Diamond and 54.7% on Humanity's Last Exam with tools - **Gemini 3.1 Pro offers best value** at $2/1M input (60% cheaper) with competitive scores on 13 of 16 benchmarks - **Long-context workloads favor GPT 5.5** due to flat pricing above 200K tokens versus 2× surcharges for competitors - **Interactive applications favor Claude Opus 4.7** with 0.5s TTFT versus 3.0s for GPT 5.5 - **No single model wins all benchmarks**—workload type determines the optimal choice - **All models struggle with UI design**, defaulting to generic card-grid layouts without explicit styling prompts *Last updated: April 25, 2026. Benchmark data sourced from OpenAI launch post, Anthropic system card, Google DeepMind model card, LLM Stats, and Artificial Analysis. All scores are self-reported by providers at highest reasoning tiers.*