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 July 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. Composite scores and any unpublished benchmark cells are editorial estimates.
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 4.8, and GPT-5.6 — represent the current frontier ceiling. No single model dominates every benchmark; instead, each claims specific territory. Gemini leads reasoning (GPQA: 94.3%), Claude leads coding and human preference (SWE-bench: 88.6% for Opus 4.8, ~95.5% for Anthropic’s Mythos-class coding specialists, #1 Arena Elo), and GPT-5.6 anchors mathematics and factuality. The A-tier is where convergence becomes tangible: GLM-5.2, Kimi K3, and Grok 4.5 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 4.8 |
Claude Sonnet 5 |
GPT 5.6 |
Grok 4.5 |
GLM-5.2 | Kimi K3 |
DS V4 |
Qwen 3.6 |
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. DeepSeek V4 offers near-frontier capability at $0.14/MTok input — roughly 36x cheaper than Claude Opus 4.8 ($5) and about 150x cheaper than a $21 frontier reasoning tier. GLM-5.2, the #1 open-weight model, lands at $1.40/MTok. 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 |
|---|---|---|---|---|
| Gemini Flash-Lite | $0.10 | $0.40 | Closed | |
| DeepSeek V4 | $0.14 | $0.28 | Open | |
| GPT-5.6 Luna | $1.00 | $6.00 | Closed | |
| GLM-5.2 | $1.40 | $4.40 | Open MIT | |
| Gemini 3.1 Pro | $2.00 | $12.00 | Closed | |
| Claude Sonnet 5 | $2.00 | $10.00 | Closed | |
| Grok 4.5 | $2.00 | $6.00 | Closed | |
| Opus 4.8 / GPT-5.6 Sol | $5.00 | $25–30 | Closed | |
| Claude Fable 5 (Mythos) | $10.00 | $50.00 | Closed | |
| Frontier Pro reasoning (est.) | ~$21 | ~$105 | Closed |
| Benchmark | Leader | Gemini 3.1 Pro |
Claude Opus 4.8 |
GPT 5.6 |
Grok 4.5 |
GLM-5.2 | Kimi K3 |
DS V4 |
Qwen 3.6 |
|---|---|---|---|---|---|---|---|---|---|
| ARC-AGI-2 | Gemini | 0.0 | -8.3 | -22.9 | — | — | — | — | — |
| GPQA Diamond | Gemini | 0.0 | -3.3 | — | -5.3 | -3.1 | — | — | — |
| AIME 2025 | GPT/Grok | -5.0 | — | 0.0 | 0.0 | — | -3.9 | -4.0 | -8.0 |
| SWE-bench | Mythos 5 | -13.5 | -6.9 | -10.5 | — | -7.5 | -8.5 | — | — |
| HLE (tools) | GLM-5.2 | — | — | -4.9 | — | 0.0 | -0.2 | — | — |
| HMMT 2025 | Grok 4.5 | — | — | — | 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 | current |
| Claude Opus 4.8 | Anthropic | Undisclosed | Undisclosed | Dense (est.) | 1M tokens | AWS Trainium/GPU | Proprietary | current |
| Claude Sonnet 5 | Anthropic | Undisclosed | Undisclosed | Dense (est.) | 1M tokens | AWS Trainium/GPU | Proprietary | Jun 30 2026 |
| GPT-5.6 | OpenAI | Undisclosed | Undisclosed | Dense + CoT | 256K tokens | NVIDIA H100/H200 | Proprietary | Jun/Jul 2026 |
| Grok 4.5 | xAI | Undisclosed | Undisclosed | Dense (est.) | 256K+ tokens | Colossus (H100) | Proprietary | Jul 2026 |
| GLM-5.2 | Zhipu AI | 744B (est.) | Undisclosed | MoE (est.) | 1M tokens | Huawei Ascend | MIT | current |
| Kimi K3 | Moonshot AI | ~2.8T | ~32B (est.) | MoE | 128K tokens | NVIDIA | Open weight | Jul 16 2026 |
| DeepSeek V4 | DeepSeek | 671B (est.) | 37B (est.) | MoE | 128K tokens | NVIDIA H800 | Open weight | Apr 2026 |
| Qwen 3.6 | Alibaba | 235B | 22B | MoE | 128K tokens | NVIDIA/Alibaba | Apache 2.0 | 2026 |
| 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 leads reasoning (GPQA: 94.3%), Anthropic dominates coding (SWE-bench: 88.6% for Opus 4.8, ~95.5% for its Mythos-class specialists) and human preference (#1 Arena), and GPT-5.6 anchors math and factuality. Choosing the "best" model depends entirely on your use case.
Open-Source Reaches Frontier
On GPQA Diamond, GLM-5.2 (91.2%, open MIT) now sits about 3 points behind Gemini 3.1 Pro (94.3%). On Humanity's Last Exam, every model — open and closed — still scores below 51%, and the best open weights sit within a few points of the frontier. The gap 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-5.2, Kimi K3, Qwen 3.6, DeepSeek V4). Qwen alone has 700M+ downloads. A large share of AI startups build on Chinese open-source. The West leads closed models; China leads open models.
Pricing Spread: 100–350x Between Tiers
Cheap capable open models run at $0.10–0.14/MTok input (Gemini Flash-Lite, DeepSeek V4), while a frontier reasoning tier reaches ~$21 — a spread of roughly 150x on input and far wider on output (DeepSeek V4 $0.28 vs Fable 5 $50 ≈ 180x). Cost is rapidly becoming a routing decision, not a differentiator.
Agentic = New Battleground
Kimi K3 executes hundreds of sequential tool calls autonomously. Grok 4.5 uses a multi-agent collaboration system. Claude Sonnet 5 has native agentic capabilities. Models are now judged on autonomous task completion, not just Q&A.
The February 2026 Release Wave
February 2026 saw a cluster of major releases: Claude Opus 4.6, GPT-5.3 Codex, Gemini 3.1 Pro, GLM-5, Grok 4.1, and Qwen 3.5. The mid-2026 wave kept the pace: DeepSeek V4 (Apr), Claude Sonnet 5 (Jun 30) and Opus 4.8, the GPT-5.6 family (Jun/Jul), Grok 4.5 (Jul), and Kimi K3 (Jul 16). Frontier advancement is accelerating, not slowing.