Executive model board
A current snapshot of the key models, what each is best for, and the public benchmark scores that matter most when you are deciding where to spend time and budget.
Last updated 24 May 2026
TL;DR
GPT-5.5 now leads the quality index, but Gemini 3.1 Pro costs less and wins on reasoning. Claude Opus 4.7 remains best for high-stakes analysis. Open-weight models (Kimi K2.6, DeepSeek V4) are frontier-competitive for sensitive, cost-constrained work.
Best overall quality
Launched April 23, 2026. Now the leader on the Artificial Analysis Intelligence Index (score: 60). Reliable across reasoning, coding, and writing. The safest newest premium choice for organizations standardizing on OpenAI.
Best mathematical reasoning
Leads every published reasoning benchmark: GPQA Diamond 94.3%, ARC-AGI-2 77.1%, HLE (mathematical) 44.4%. The strongest all-round quality per dollar in the premium tier at $1/M input.
Best for engineering
SWE-bench Pro 64.3% — 5.7 points ahead of GPT-5.5. Only model where engineering speed and output quality justify the $5/M cost. Essential for teams building high-quality software.
Best value for frontier work
Open-weight, 1T context window, SWE-bench Pro 58.6% (matches Gemini 3.1 Pro level, within 6 points of Claude Opus 4.7). Frontier-competitive quality without vendor lock-in. Best for teams comfortable with open ecosystems.
Shared public evals
Comparison source: Best AI Models in May 2026 — buildfastwithai.com
| Eval | OpenAIGPT-5.5$2.00 in / $8.00 out | GoogleGemini 3.1 Pro$1.00 in / $10.00 out | AnthropicClaude Opus 4.7$5.00 in / $25.00 out | DeepSeekDeepSeek V4$0.14 in / $0.28 out |
|---|---|---|---|---|
GPQA DiamondGraduate-level science reasoning — frontier differentiator for research-grade work | ~92% | 94.3% | 94.2% | ~91% |
SWE-bench ProProduction-grade software engineering benchmark — real GitHub issue resolution | ~58.6% | ~71.8% | 64.3% | ~56% |
HLE (Mathematics)Humanity's Last Exam — graduate-level reasoning across all domains | 42.0% | 44.4% | ~41% | ~40% |
Context windowMaximum tokens in a single request (larger = handle full documents/knowledge bases) | 1M tokens | 1M+ tokens | 200K tokens | 256K tokens |
GPT-5.5 pricing: OpenAI pricing (May 2026)
Gemini 3.1 Pro pricing: Gemini API pricing
Claude Opus 4.7 pricing: Anthropic pricing
DeepSeek V4 pricing: DeepSeek API pricing (May 2026)
Current picks
Use this section for the buying lens: what each model is actually good for, the headline numbers worth remembering, and the standard API pricing to keep in mind.
Latest frontier model, leads quality index
Launched April 23, 2026. Now leads the Artificial Analysis Intelligence Index (score: 60). Solid all-rounder for organizations standardizing on OpenAI. The newest, most actively optimized model.
Sources: GPT-5.5 launch — OpenAI, Artificial Analysis Intelligence Index
Best reasoning, best value for premium work
Leads every published reasoning benchmark: GPQA Diamond 94.3%, ARC-AGI-2 77.1%, mathematical reasoning (HLE) 44.4%. Half the cost of Claude Opus at $1/M input. Best benchmark-per-dollar in premium tier.
Sources: Best AI Models in May 2026 — felloai.com, Gemini 3.1 Pro — Pluralsight
Best for production engineering, deep analysis
SWE-bench Pro 64.3% — the unmatched coding leader, 5.7 points ahead of GPT-5.5. Essential for teams building or maintaining quality codebases. Worth the $5/M cost when engineering productivity or code quality is the constraint.
Sources: Claude Opus 4.7 — CloudZero, Claude pricing — Anthropic
Frontier quality at minimal cost, 256K context
Frontier-competitive scores at $0.14/M input — 14–36x cheaper than proprietary alternatives. The emerging disruptor for cost-sensitive, non-sensitive workloads. Data residency and vendor risk are the real questions, not capability.
Sources: DeepSeek V4 — Open Router, DeepSeek API pricing (May 2026)
1 trillion token context, open-weight frontier quality
Open-weight, 1 trillion token context (largest in the market), SWE-bench Pro 58.6% — within 6 points of Claude Opus 4.7. No per-token vendor lock-in. Best for organizations wanting infrastructure control and document-scale processing.
Sources: Best AI Models of May 2026 — buildfastwithai.com, Kimi K2.6 — Moonshot AI
Reading the board
Scores are useful as signals, but leaders still need to map them back to workflow fit, governance, review load, and integration cost.
Benchmarks show direction, not whether a product should be bought or rolled out.
Use benchmark charts to frame diligence questions, not to shortcut buying decisions.