# Top AI Models 2026: Claude Fable 5 vs GPT-5.6 Sol vs Kimi K3 vs Qwen3.8 vs GLM-5.2 — Verified Benchmarks Head-to-head comparison of the top 5 AI models of 2026. We verify Claude Fable 5, GPT-5.6 Sol, Kimi K3, Qwen3.8 Max, and GLM-5.2 benchmarks from official sources and independent tests. Real numbers, real sources. # Top 5 AI Models of 2026: Claude Fable 5 vs GPT-5.6 Sol vs Kimi K3 vs Qwen3.8 vs GLM-5.2 By ZVHH Research | July 23, 2026 | 18 min read ### TL;DR — Key Findings - **Claude Fable 5** leads in scientific reasoning, long-horizon agentic work, and safety — but its aggressive safeguards cause fallbacks that hurt real-world performance - **GPT-5.6 Sol** offers the best price-to-performance ratio, leading in coding efficiency (one-third the cost of Fable 5) with near-parity on most benchmarks - **Kimi K3** is the first open 3T-class model with exceptional frontend coding and 1M context — but weights are not yet public and API pricing is steep - **Qwen3.8 Max** is a preview with no published benchmarks — strong value proposition if it delivers on its open-weight promise - **GLM-5.2** is the strongest open-weight model for long-context coding, beating GPT-5.5 on SWE-bench Pro at 62.1 ## At a Glance: Model Specifications Claude Fable 5 Anthropic · Released June 9, 2026 Closed Mythos Class 1M Context GPT-5.6 Sol OpenAI · Released July 9, 2026 Closed 1M Context Multi-Agent Kimi K3 Moonshot AI · Released July 16, 2026 Open Weights Pending 2.8T MoE 1M Context Qwen3.8 Max Alibaba / Qwen · Preview July 2026 Preview 2.4T Params Open Promise GLM-5.2 Z.ai (Zhipu) · Released June 16, 2026 Open Weight 1M Context ### Architecture Comparison | **Feature** | **Claude Fable 5** | **GPT-5.6 Sol** | **Kimi K3** | **Qwen3.8 Max** | **GLM-5.2**| --- | --- | --- | --- | --- | --- | **Total Parameters** | Not disclosed | Not disclosed | 2.8T | ~2.4T (claimed) | Not disclosed| | **Architecture** | Not disclosed | Not disclosed | Sparse MoE (16/896 experts) | Not disclosed | Not disclosed| | **Context Window** | 1,048,576 | 1,048,576 | 1,048,576 | Not confirmed | 1,048,576| | **Max Output** | 128,000 | Not disclosed | Not disclosed | Not disclosed | 131,072| | **Modalities** | Text, Vision | Text, Vision | Text, Vision | Text, Vision, Video | Text only| | **Knowledge Cutoff** | Jan 2026 | Not disclosed | Not disclosed | Not disclosed | Not disclosed| | **Open Weights** | No | No | Pending (July 27) | Promise (unconfirmed) | Yes| Source: [Anthropic](https://www.anthropic.com/news/claude-fable-5-mythos-5), [OpenAI](https://openai.com/index/gpt-5-6/), [Moonshot AI](https://www.kimi.com/blog/kimi-k3), [Z.AI](https://docs.z.ai/guides/llm/glm-5.2), [WhatLLM](https://whatllm.org/blog/kimi-k3), [BuildFastWithAI](https://www.buildfastwithai.com/blogs/qwen3-8-preview-2-4t-params-open-weights-release) ## Benchmark Comparison The following tables consolidate benchmark data from official release posts and independent evaluators. Where models were tested under different harnesses (Claude Code, Codex, KimiCode), results are reported as published with caveats noted. HIGH confidence = verified by 2+ sources. MEDIUM confidence = vendor-reported, 1 independent cross-check. ### Reasoning & General Intelligence | **Benchmark** | **Claude Fable 5** | **GPT-5.6 Sol** | **Kimi K3** | **GLM-5.2** | **Source**| --- | --- | --- | --- | --- | --- | **GPQA Diamond** | 92.6% | 94.6% | 93.5% | Not reported | [OpenAI](https://openai.com/index/gpt-5-6/), [Moonshot](https://www.kimi.com/blog/kimi-k3)| | **Artificial Analysis Intelligence Index v4.1** | 59.9 | 58.9 | 57.1 | 51.0 | [Artificial Analysis](https://artificialanalysis.ai/)| | **GDPval-AA v2 (Elo)** | 1,760 | 1,748 | 1,668 | Not reported | [Artificial Analysis](https://artificialanalysis.ai/), [Moonshot](https://www.kimi.com/blog/kimi-k3)| | **Humanity's Last Exam (HLE)** | 53.3% | 44.5% | 43.5% | Not reported | [Moonshot](https://www.kimi.com/blog/kimi-k3)| | **FrontierMath Tier 1-3** | Not reported | 89.0% | Not reported | Not reported | [OpenAI](https://openai.com/index/gpt-5-6/)| | **FrontierMath Tier 4** | 87.8% | 83.0% | Not reported | Not reported | [OpenAI](https://openai.com/index/gpt-5-6/)| ### Coding & Software Engineering | **Benchmark** | **Claude Fable 5** | **GPT-5.6 Sol** | **Kimi K3** | **GLM-5.2** | **Source**| --- | --- | --- | --- | --- | --- | **Terminal-Bench 2.1** | 83.1% | 88.8% | 88.3% | 81.0% | [OpenAI](https://openai.com/index/gpt-5-6/), [Moonshot](https://www.kimi.com/blog/kimi-k3), [Z.AI](https://docs.z.ai/guides/llm/glm-5.2)| | **Terminal-Bench 2.1 (Ultra)** | Not reported | 91.9% | Not reported | Not reported | [OpenAI](https://openai.com/index/gpt-5-6/)| | **DeepSWE v1.1** | 69.7% | 72.7% | 67.5% | Not reported | [OpenAI](https://openai.com/index/gpt-5-6/), [Moonshot](https://www.kimi.com/blog/kimi-k3)| | **SWE-bench Pro** | 80.0% | 64.6% | Not reported | 62.1% | [MorphLLM](https://www.morphllm.com/claude-benchmarks), [OpenAI](https://openai.com/index/gpt-5-6/), [Z.AI](https://docs.z.ai/guides/llm/glm-5.2)| | **SWE Marathon** | 35.0% | 39.0% | 42.0% | Not reported | [Moonshot](https://www.kimi.com/blog/kimi-k3)| | **Program Bench** | 76.8% | 77.6% | 77.8% | Not reported | [Moonshot](https://www.kimi.com/blog/kimi-k3)| | **Artificial Analysis Coding Agent Index** | 77.2 | 80.0 | 76.2 | Not reported | [Artificial Analysis](https://artificialanalysis.ai/)| **Note:** SWE-bench Pro scores for Fable 5 are from independent testing. Fable 5 hit fallbacks on 35% of SWE Marathon tasks per Moonshot's evaluation, which may suppress its measured score. MEDIUM confidence ### Agentic Work & Tool Use | **Benchmark** | **Claude Fable 5** | **GPT-5.6 Sol** | **Kimi K3** | **GLM-5.2** | **Source**| --- | --- | --- | --- | --- | --- | **Agents' Last Exam** | 40.5% | 52.7% | Not reported | Not reported | [OpenAI](https://openai.com/index/gpt-5-6/)| | **BrowseComp** | 88.0% | 90.4% | 91.2% | Not reported | [Anthropic](https://www.anthropic.com/news/claude-fable-5-mythos-5), [OpenAI](https://openai.com/index/gpt-5-6/), [Moonshot](https://www.kimi.com/blog/kimi-k3)| | **MCP Atlas** | 84.7% | 83.6% | 84.2% | Not reported | [Moonshot](https://www.kimi.com/blog/kimi-k3)| | **AutomationBench** | 17.4% | 18.1% | Not reported | Not reported | [OpenAI](https://openai.com/index/gpt-5-6/)| | **OSWorld 2.0** | Not reported | 62.6% | Not reported | Not reported | [OpenAI](https://openai.com/index/gpt-5-6/)| ### Science & Multimodal | **Benchmark** | **Claude Fable 5** | **GPT-5.6 Sol** | **Kimi K3** | **GLM-5.2** | **Source**| --- | --- | --- | --- | --- | --- | **MMMU-Pro** | 81.2% | 83.0% | 81.6% | Not reported | [OpenAI](https://openai.com/index/gpt-5-6/), [Moonshot](https://www.kimi.com/blog/kimi-k3)| | **GeneBench Pro** | Refuses | 28.7% | Not reported | Not reported | [OpenAI](https://openai.com/index/gpt-5-6/)| | **LifeSciBench** | Refuses | 59.9% | Not reported | Not reported | [OpenAI](https://openai.com/index/gpt-5-6/)| | **OmniDocBench** | 89.8% | 85.8% | 91.1% | Not reported | [Moonshot](https://www.kimi.com/blog/kimi-k3)| **Note:** Claude Fable 5's biology classifiers cause it to refuse most life science queries, falling back to Opus 4.8. This is a safeguard limitation, not a capability gap. HIGH confidence ## Pricing Comparison Claude Fable 5 $10/M input $50/M output Mythos class pricing GPT-5.6 Sol $5/M input $30/M output ~1/3 cost of Fable 5 GPT-5.6 Terra $2.50/M input $15/M output Everyday work GPT-5.6 Luna $1/M input $6/M output Most cost-efficient Kimi K3 $3/M input ($0.30 cached) $15/M output 90%+ cache hit rate GLM-5.2 $1.40/M input ($0.26 cached) $4.40/M output Best open-model value Source: [Anthropic](https://www.anthropic.com/news/claude-fable-5-mythos-5), [OpenAI](https://openai.com/index/gpt-5-6/), [Moonshot AI](https://www.kimi.com/blog/kimi-k3), [Zvi Mowshowitz](https://thezvi.substack.com/p/glm-52-is-the-new-best-open-model) ## Model-by-Model Analysis ### 1. Claude Fable 5 — The Scientific Powerhouse Released June 9, 2026, Claude Fable 5 is Anthropic's first Mythos-class model made safe for general use. It sits above the Opus tier and represents the most capable model Anthropic has released to the public. **Strengths:** Fable 5 leads in scientific reasoning (HLE 53.3%), deep software engineering (SWE-bench Pro 80% independent), and long-horizon autonomous work. It was the first model to break 90% on Hebbia's analytics benchmark. On FrontierCode, it scores highest among all frontier models. Customer feedback from Stripe, GitHub, and Cursor confirms it "compressed months of engineering into days." > "Claude Fable 5 is the state of the art model on CursorBench. It's opened up a class of long-horizon problems that were out of reach for earlier models." — Michael Truell, CEO and Co-founder, [Anthropic announcement](https://www.anthropic.com/news/claude-fable-5-mythos-5) > "Claude Fable 5 feels materially different. In blind review, our lawyers found its redlines matched or beat our current model every time." — Aveek Duttagupta, Member of Technical Staff, [Anthropic announcement](https://www.anthropic.com/news/claude-fable-5-mythos-5) **Weaknesses:** Fable 5's aggressive safety classifiers cause it to fall back to Opus 4.8 on approximately 5% of sessions — particularly on cybersecurity, biology, chemistry, and distillation topics. This fallback degrades the user experience and can suppress benchmark scores (35% of SWE Marathon tasks triggered fallbacks per Moonshot's testing). The 30-day data retention requirement for business customers is also more restrictive than competitors. **Community sentiment:** The Reddit community has been sharply divided. Many developers report using both Fable 5 and GPT-5.6 Sol, with Fable preferred for planning and complex reasoning, while Sol is preferred for coding execution. MEDIUM confidence ### 2. GPT-5.6 Sol — The Efficiency Champion Released July 9, 2026, GPT-5.6 Sol is OpenAI's new flagship. It achieves state-of-the-art results across coding, knowledge work, cybersecurity, and science while using fewer tokens and at lower estimated cost than competing frontier models. **Strengths:** GPT-5.6 Sol leads in coding efficiency — using less than half the output tokens and taking less than half the time compared to Fable 5, at approximately one-third the cost. It sets new SOTA on Terminal-Bench 2.1 (91.9% with ultra), BrowseComp (92.2%), and Agents' Last Exam (53.6%). Its multi-agent "ultra" mode coordinates four agents in parallel for demanding tasks. It is the only model that scored well across biology (GeneBench Pro 28.7%) without refusing queries. > "Not sure (yet) if Sol is really better, but it WORKS better. Best of both worlds: Fable for planning or complex problems, GPT 5.6 SOL for execution." — Reddit user, [r/ClaudeCode](https://www.reddit.com/r/ClaudeCode/comments/1us0tzl/not_sure_yet_if_sol_is_really_better_but_it_works/) > "Fable stays and the extra 50% Claude Code usage... The problem is that Fable flags anything that has the word security in it. It demotes too much to Opus." — Reddit user, [r/Anthropic](https://www.reddit.com/r/Anthropic/comments/1urx438/has_anyone_been_able_to_test_gpt_56_sol_vs_fable/) **Weaknesses:** SWE-bench Pro (64.6%) trails Fable 5's 80% by a wide margin. GPQA Diamond (94.6%) is strong but Fable 5 and Opus 4.8 are comparable. The model's cybersecurity safeguards block roughly ten times more potentially harmful activity than previous models, which some users report as friction for legitimate work. ### 3. Kimi K3 — The Open 3T Challenger Released July 16, 2026, Kimi K3 is Moonshot AI's 2.8 trillion parameter flagship — the first open 3T-class model. Built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), it activates only 16 of 896 experts per token via Stable LatentMoE. **Strengths:** K3 debuted at #1 on Arena's blind frontend coding ranking (1,679 score) ahead of Claude Fable 5. It leads on SWE Marathon (42.0%), OmniDocBench (91.1%), and BrowseComp (91.2%). Its kernel optimization results were competitive with Fable 5 and substantially outperformed Opus 4.8 and GPT-5.6 Sol. The 1M context window is genuine and practically usable. Full weights are promised by July 27, 2026. > "K3 is one of the most important model releases of 2026 because it combines three things that rarely arrive together: near-frontier coding, a genuine million-token serving target, and a promised open checkpoint at unprecedented scale." — [WhatLLM.org](https://whatllm.org/blog/kimi-k3) **Weaknesses:** K3 still trails Fable 5 and GPT-5.6 Sol overall, as Moonshot itself acknowledges. API pricing ($3 input, $15 output) is steep. The weights are not yet public (as of July 23, promised by July 27). Launch capacity was strained, with new subscriptions temporarily paused on July 20. It requires a 64+ accelerator supernode for self-hosting (~1.4TB weights at 4-bit). Sensitivity to thinking history can cause instability when switching models mid-session. MEDIUM confidence ### 4. Qwen3.8 Max — The Preview With Potential Qwen3.8-Max-Preview is available through Alibaba's Token Plan, Qoder, and QoderWork. The teaser claims 2.4 trillion parameters and an open-weight release, positioning it second only to Claude Fable 5. However, as of July 23, no official benchmark table, model card, or technical report has been published. **What we know:** The preview is confirmed real and purchasable. Qwen's lineage shows a consistent cadence of Max-tier releases every 4-6 weeks. Qwen3.7-Max (the current verified baseline) scores 92.4 on GPQA Diamond, 80.4% on SWE-bench Verified, and costs $1.25/$3.75 per million tokens — one-eighth of Fable 5's input price. The open-weight promise would break Alibaba's pattern: both Qwen3.7-Max and Qwen3.6-Max-Preview shipped closed. > "Qwen3.8 is the rare launch where the product is confirmed real and every single performance claim about it is unconfirmed. You can buy access today and still not know what you are buying." — [BuildFastWithAI](https://www.buildfastwithai.com/blogs/qwen3-8-preview-2-4t-params-open-weights-release) **Early testing:** A blind comparison by Trilogy AI scored Kimi K3 at 83 vs Qwen3.8 at 80 on a 269-file repository task. Qwen was stronger in system-boundary and replay-metadata decisions; Kimi finished faster with fewer tokens. This is one data point, not a benchmark. The Reddit community's early impressions were "a bit unimpressed" with mixed follow-up results. LOW confidence **What to watch for:** Official benchmark table, active parameter count, Hugging Face repo with license file, published API pricing, and independent evaluation from Artificial Analysis or similar. ### 5. GLM-5.2 — The Open-Weight Coding Specialist Released June 16, 2026, GLM-5.2 by Z.ai (Zhipu AI) is the strongest open-weight model for long-horizon coding tasks. Its headline achievement: SWE-bench Pro at 62.1, edging past GPT-5.5's 58.6 and improving on GLM-5.1's 58.4 by a wide margin. **Strengths:** Terminal-Bench 2.1 at 81.0 (within 4 points of Opus 4.8 at 85.0). FrontierSWE at 74.4 (one notch behind Opus 4.8's 75.1%). PostTrainBench at #1, ahead of Opus 4.8. The 1M token context is genuinely usable for project-scale engineering. At $1.40/$0.26/$4.40 (input/cached-input/output), it offers the best value among frontier-class models. It is fully open-weight and self-hostable. > "GLM 5.2 is a marvel! It is at least as good as Opus 4.8 and GPT 5.5. It's super fast, inexpensive, and not too verbose. It handles long context VERY well. I've never experienced an open weights model like this before." — Jeremy Howard, [via TheZvi Substack](https://thezvi.substack.com/p/glm-52-is-the-new-best-open-model) > "I asked it to implement custom error pages for Envoy Gateway in bare metal Kubernetes cluster. GLM-5.2 took 2 hours and managed it. Opus 4.8 high couldn't do it yesterday and confidently hallucinated external reasons for failure. Cost: $7.32." — [X/Marcus Wadas](https://thezvi.substack.com/p/glm-52-is-the-new-best-open-model) **Weaknesses:** No native vision support (a significant limitation for multimodal tasks). The model appears heavily distilled from Claude, which means it may share Claude's blind spots and generalize poorly on less targetable tasks. Several users report "LLM-isms" and a "benchmaxxed" feel — strong on puzzle-like challenges, weaker on real-world ambiguity. It is not cheap enough for bulk tasks compared to smaller open models. MEDIUM confidence ## Where Each Model Wins Best Overall Intelligence #### Claude Fable 5 Unmatched in scientific reasoning, HLE (53.3%), and long-horizon autonomous work. Best for research, planning, and complex problem-solving where capability matters more than cost. Best Value & Efficiency #### GPT-5.6 Sol One-third the cost of Fable 5 with near-parity on most benchmarks. Best for production use where cost-per-task and speed matter. Ultra mode adds multi-agent power for hard tasks. Best for Frontend & Coding #### Kimi K3 #1 on Arena's frontend coding with 1,679 score. Exceptional terminal work and long-horizon agent tasks. Best when you need open weights and a 1M context window. Best Value Potential #### Qwen3.8 Max If it delivers on its open-weight promise and maintains Qwen's value pricing (~$1.25 input), it could be the best price-to-performance model. But benchmarks are unconfirmed. Best Open-Weight Model #### GLM-5.2 Strongest open model for long-context coding. Beats GPT-5.5 on SWE-bench Pro at 62.1. Best for developers who need self-hosting and can work without vision. ## How We Tested This All benchmark data in this article comes from official release posts (Anthropic, OpenAI, Moonshot AI, Z.AI) and independent evaluators (Artificial Analysis, WhatLLM, MorphLLM, EvoLink, BuildFastWithAI, Trilogy AI). Where models were evaluated under different agentic harnesses (Claude Code, Codex, KimiCode), we report the published scores with the harness noted. We do not normalize across harnesses — this would introduce false precision. Community quotes are from Reddit (r/ClaudeCode, r/Anthropic, r/OpenAI, r/LocalLLaMA), X/Twitter, and independent blogs. Each is attributed to its source with a link. We label confidence levels: HIGH = verified by 2+ independent sources, MEDIUM = vendor-reported with 1 independent cross-check, LOW = single data point or early testing. This article follows ZVHH's editorial standards: no hype language, no fabricated numbers, no unverified claims presented as fact. If a number is not published, we say so. ### Primary Sources - [Anthropic — Claude Fable 5 and Claude Mythos 5](https://www.anthropic.com/news/claude-fable-5-mythos-5) (June 9, 2026) - [OpenAI — GPT-5.6: Frontier Intelligence That Scales With Your Ambition](https://openai.com/index/gpt-5-6/) (July 9, 2026) - [Moonshot AI — Kimi K3: Open Frontier Intelligence](https://www.kimi.com/blog/kimi-k3) (July 16, 2026) - [Z.AI — GLM-5.2 Developer Documentation](https://docs.z.ai/guides/llm/glm-5.2) - [WhatLLM — Kimi K3: Benchmarks, Pricing, API, Context Window](https://whatllm.org/blog/kimi-k3) (July 20, 2026) - [Artificial Analysis — Intelligence Index & Coding Agent Index](https://artificialanalysis.ai/) - [MorphLLM — Claude Benchmarks 2026](https://www.morphllm.com/claude-benchmarks) - [BuildFastWithAI — Qwen3.8 Preview: 2.4T Params, Open Weights](https://www.buildfastwithai.com/blogs/qwen3-8-preview-2-4t-params-open-weights-release) (July 19, 2026) - [TheZvi — GLM-5.2 Is The New Best Open Model](https://thezvi.substack.com/p/glm-52-is-the-new-best-open-model) (June 22, 2026) - [EvoLink — Qwen3.8 Benchmark: Evidence, Gaps & Early Tests](https://evolink.ai/blog/qwen3-8-benchmark) (July 21, 2026) - [Reddit r/ClaudeCode — Community Discussion](https://www.reddit.com/r/ClaudeCode/comments/1us0tzl/not_sure_yet_if_sol_is_really_better_but_it_works/) - [Reddit r/Anthropic — Honest Review of GPT-5.6 and Fable](https://www.reddit.com/r/Anthropic/comments/1uvor7l/an_honest_review_of_gpt_56_and_fable/) ### Related Coverage [Claude Fable 5: Anthropic's Mythos-Class Model](/claude-fable-5-anthropic-mythos-class-model.html) [GPT-5.6 Sol: OpenAI's Frontier Intelligence](/gpt-5-6-sol-openai-frontier-intelligence.html) [Kimi K3: Open Frontier Intelligence](/kimi-k3-open-frontier-intelligence.html) [GLM-5.2: Long-Horizon Coding](/glm-5-2-zai-long-horizon-coding.html) [Qwen3.8 Max Preview: Alibaba's 2.4T Model](/qwen3-8-max-preview-alibaba-2-4t.html)