Frontier AI Models — Comprehensive Comparison
This companion analysis breaks down the current model landscape by tier, mapping the specific strengths, weaknesses, and cost profiles of every frontier model available in February 2026. Where the primary convergence analysis tracks how the gap has closed, this page answers the practical question: which models belong in which tier, and why.
The tier framework below classifies models into four levels — S, A, B, and C — based on aggregate benchmark performance, real-world capability, and competitive positioning. The critical finding: S-tier is no longer the exclusive province of closed models. Open-weight models now occupy A-tier convincingly, and on select benchmarks, individual open models outperform every closed alternative. For the detailed convergence trajectory and economic analysis, see Chapter 3: The Great Convergence.
Reading the Tier Map
S-tier models — Gemini 3.1 Pro, Claude Opus 4.6, and GPT-5.2 — represent the current frontier ceiling. No single model dominates every benchmark; instead, each claims specific territory. Gemini leads reasoning (ARC-AGI-2: 77.1%), Claude leads coding and human preference (SWE-bench: 82.1%, #1 Arena Elo), and GPT-5.2 leads mathematics and factuality (AIME: 100%, hallucination: 6.2%). The A-tier is where convergence becomes tangible: GLM-5, Kimi K2.5, and Grok 4 deliver S-tier-adjacent performance at dramatically lower cost — or in the case of open models, at near-zero marginal cost when self-hosted.
Beyond Single Scores
Benchmarks capture isolated capabilities, but enterprise deployment demands multi-dimensional strength. The capability heatmap below reveals what aggregate scores obscure: models that appear similar on headline numbers often diverge sharply in domain-specific performance. This is why model routing — matching the right model to the right task — matters more than selecting a single “best” model.
| Capability | Gemini 3.1 Pro |
Claude Opus 4.6 |
Claude Sonnet 5 |
GPT 5.2 |
Grok 4/4.1 |
GLM-5 | Kimi K2.5 |
DS Speciale |
Qwen 3 |
Llama 4 |
OLMo 3.1 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| REASONING & KNOWLEDGE | |||||||||||
| Logical Reasoning | 5 | 4 | 4 | 4 | 4 | 3 | 3 | 3 | 3 | 2 | 2 |
| Scientific Knowledge | 5 | 5 | 3 | 4 | 4 | 3 | 3 | 3 | 3 | 2 | 2 |
| Mathematics | 5 | 4 | 4 | 5 | 5 | 3 | 5 | 5 | 4 | 2 | 3 |
| CODING & ENGINEERING | |||||||||||
| Code Generation | 3 | 5 | 5 | 5 | 3 | 4 | 4 | 3 | 3 | 2 | 2 |
| Bug Fixing (SWE) | 3 | 5 | 5 | 5 | 3 | 4 | 4 | 3 | 3 | 2 | 2 |
| AGENTIC & TOOL USE | |||||||||||
| Tool Calling | 4 | 5 | 5 | 5 | 4 | 4 | 5 | 3 | 3 | 2 | 2 |
| Multi-step Agents | 4 | 5 | 5 | 5 | 4 | 4 | 5 | 3 | 3 | 2 | 1 |
| LANGUAGE & CREATIVITY | |||||||||||
| Creative Writing | 4 | 5 | 4 | 4 | 5 | 3 | 3 | 2 | 3 | 3 | 2 |
| Multilingual | 4 | 4 | 4 | 4 | 3 | 4 | 3 | 3 | 5 | 4 | 2 |
| MULTIMODAL | |||||||||||
| Vision/Image | 5 | 4 | 3 | 4 | 3 | 3 | 5 | 2 | 4 | 3 | 1 |
| RELIABILITY | |||||||||||
| Low Hallucination | 4 | 4 | 4 | 5 | 4 | 2 | 2 | 3 | 3 | 3 | 3 |
| Long Context | 5 | 5 | 5 | 4 | 4 | 3 | 3 | 3 | 3 | 5 | 2 |
| Instruction Following | 5 | 5 | 5 | 5 | 4 | 4 | 4 | 3 | 4 | 3 | 3 |
The Cost-Capability Equation
The pricing table below makes the strategic calculus explicit. GLM-5 offers near-frontier capability at $0.11/MTok — 45x cheaper than Claude Opus 4.6 and 136x cheaper than GPT-5.2 reasoning mode. For enterprises processing millions of tokens daily, this price disparity translates to order-of-magnitude differences in operating cost. The implication is clear: organizations that design for model portability can arbitrage this pricing spread, routing premium tasks to S-tier models and commodity tasks to cost-effective open alternatives.
| Model | Input $/MTok | Output $/MTok | Type | Cost vs Performance |
|---|---|---|---|---|
| GLM-5 | $0.11 | ~$0.44 | Open MIT | |
| DeepSeek V3.2 | $0.27 | ~$1.10 | Open | |
| Gemini 3 Flash | $0.50 | $1.50 | Closed | |
| Gemini 3.1 Pro | $2.00 | $12.00 | Closed | |
| GPT-5.2 (std) | $2.50 | $10.00 | Closed | |
| Claude Sonnet 5 | $3.00 | $15.00 | Closed | |
| Claude Opus 4.5/4.6 | $5.00 | $25.00 | Closed | |
| GPT-5.2 Reasoning | $15.00 | $75.00 | Closed |
| Benchmark | Leader | Gemini 3.1 Pro |
Claude Opus 4.6 |
GPT 5.2 |
Grok 4 |
GLM-5 | Kimi K2.5 |
DS Spec. |
Qwen 3 |
|---|---|---|---|---|---|---|---|---|---|
| ARC-AGI-2 | Gemini | 0.0 | -8.3 | -22.9 | — | — | — | — | — |
| GPQA Diamond | Gemini | 0.0 | -3.0 | — | -5.4 | — | — | — | — |
| AIME 2025 | GPT/Grok | -5.0 | — | 0.0 | 0.0 | — | -3.9 | -4.0 | -8.0 |
| SWE-bench | Sonnet 5 | -5.9 | -1.3 | -2.1 | — | -4.3 | -5.3 | — | — |
| HLE (tools) | GLM-5 | — | — | -4.9 | — | 0.0 | -0.2 | — | — |
| HMMT 2025 | Grok 4 | — | — | — | 0.0 | — | — | — | -13.7 |
| Model | Organization | Parameters | Active Params | Architecture | Context Window | Training Hardware | License | Release |
|---|---|---|---|---|---|---|---|---|
| Gemini 3.1 Pro | Google DeepMind | Undisclosed | Undisclosed | Dense (est.) | ~2M tokens | TPU v5p/v6 | Proprietary | Feb 2026 |
| Claude Opus 4.6 | Anthropic | Undisclosed | Undisclosed | Dense (est.) | 1M tokens | AWS Trainium/GPU | Proprietary | Feb 2026 |
| Claude Sonnet 5 | Anthropic | Undisclosed | Undisclosed | Dense (est.) | 1M tokens | AWS Trainium/GPU | Proprietary | Feb 2026 |
| GPT-5.2 | OpenAI | Undisclosed | Undisclosed | Dense + CoT | 256K tokens | NVIDIA H100/H200 | Proprietary | Dec 2025 |
| Grok 4/4.1 | xAI | Undisclosed | Undisclosed | Dense (est.) | 256K+ tokens | Colossus (H100) | Proprietary | 2025 |
| GLM-5 | Zhipu AI | 744B | Undisclosed | MoE (est.) | 128K tokens | Huawei Ascend | MIT | Feb 2026 |
| Kimi K2.5 | Moonshot AI | 1T | ~32B (est.) | MoE | 128K tokens | NVIDIA | Open weight | Jan 2026 |
| DS Speciale | DeepSeek | 671B | 37B | MoE (256 exp) | 128K tokens | NVIDIA H800 | Open weight | 2025 |
| Qwen3-235B | Alibaba | 235B | 22B | MoE | 128K tokens | NVIDIA/Alibaba | Apache 2.0 | Apr 2025 |
| Llama 4 Scout | Meta | 109B | 17B | MoE (16 exp) | 10M tokens | NVIDIA H100 | Meta License | 2025 |
| OLMo 3.1 | AI2 | 32B | 32B | Dense | 32K tokens | NVIDIA | Fully Open | 2025 |
Strategic Takeaway
The findings below reinforce the central thesis of the convergence analysis: no single model wins across all dimensions, and the gap between open and closed is no longer generational. Enterprise strategy should be built around model portability, not provider lock-in. The organizations that will extract the most value from AI in the coming years are those that can fluidly route between tiers based on task requirements, cost constraints, and latency needs.
No Single Winner
Gemini dominates reasoning (ARC-AGI-2: 77.1%), Claude dominates coding (SWE-bench: 82.1%) and human preference (#1 Arena), GPT-5.2 dominates math (AIME: 100%) and hallucination (6.2%). Choosing the "best" model depends entirely on your use case.
Open-Source Reaches Frontier
GLM-5 and Kimi K2.5 beat Claude Opus 4.5 AND GPT-5.2 on Humanity's Last Exam (50.4% vs 45.5%). Open models now lead specific benchmarks. The gap between open and closed is no longer a generation — it's often single digits.
Chinese Dominance in Open-Source
4 of the top 5 open models are Chinese (GLM-5, Kimi K2.5, Qwen, DeepSeek). Qwen alone has 700M+ downloads. 80% of AI startups build on Chinese open-source. The West leads closed models; China leads open models.
Pricing Collapse: 200x/year
GLM-5 offers frontier performance at $0.11/MTok — that's 45x cheaper than Claude Opus ($5/MTok) and 136x cheaper than GPT-5.2 reasoning ($15/MTok). Cost is rapidly becoming irrelevant as a differentiator.
Agentic = New Battleground
Kimi K2.5 executes 200-300 sequential tool calls autonomously. Grok 4.20 uses a 4-agent collaboration system. Claude Sonnet 5 has native agentic capabilities. Models are now judged on autonomous task completion, not just Q&A.
Feb 2026: Most Explosive Month Ever
Seven major model releases in one month: Claude Sonnet 5, Claude Opus 4.6, GPT-5.3 Codex, Gemini 3.1 Pro, GLM-5, Grok 4.20, Qwen 3.5. The pace of frontier advancement is accelerating, not slowing.