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LLM Comparisons · 7 min read

Ultimate 2026 GPT-5.6 Benchmarks: Math Performance vs Claude Fable on Erdős Problems

Explore the current verified landscape of frontier LLMs and math benchmarks. Discover why GPT-5.6 data remains unavailable and what this means for researchers evaluating models on long-standing problems like those from Erdős.

RA
Rai Ansar
Jul 14, 2026 · Founder, AIToolRanked
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Ultimate 2026 GPT-5.6 Benchmarks: Math Performance vs Claude Fable on Erdős Problems

GPT-5.6 does not appear among the verified frontier models on 2026-07-01.

What is the current frontier LLM landscape in 2026?

GPT-5.6 receives no listing in the 2026-07-01 frontier set. Current OpenAI entries comprise GPT-5.5, GPT-5.5 Pro, and GPT-5.3 Codex. Claude Fable 5 holds confirmed frontier status alongside 15 additional models.

GPT-5.5 Pro records explicit presence in the verified list. GPT-5.3 Codex operates as the dedicated coding variant. Claude Sonnet 4.6 maintains active frontier classification. Claude Opus 4.8 supplies separate high-context capacity. Qwen qwen3.7-plus and Qwen3.7 Max deliver documented multilingual coverage. Kimi K2.7 and MiniMax M3 add further Chinese-origin options. Grok 4.3 and Grok 4.20 appear with CLI tooling. Gemini 3.5 Flash and Gemini 3.1 Pro complete the Google entries. Mistral Medium 3.5 and DeepSeek V4 Pro round out the 18-model roster. Anthropic claude-sonnet-5 pairs with Claude Sonnet 4.6 under the same provider entity. Claude Fable 5 carries the Fable variant attribute distinct from Opus 4.8. Cursor 2 pairs with GPT-5.5 Pro at context length 200000 tokens and Claude Opus 4.8 at context length 200000 tokens. GitHub Copilot pairs with GPT-5.3 Codex at context length 128000 tokens and Grok 4.3 at context length 128000 tokens. Claude Code pairs with Claude Fable 5 at context length 200000 tokens and Claude Sonnet 4.6 at context length 128000 tokens. Grok Build CLI pairs with Grok 4.20 at context length 128000 tokens and Grok 4.3 at context length 128000 tokens. OpenAI Codex CLI pairs with GPT-5.5 at context length 128000 tokens and GPT-5.3 Codex at context length 128000 tokens. Gemini CLI pairs with Gemini 3.5 Flash at context length 1000000 tokens and Gemini 3.1 Pro at context length 1000000 tokens. Windsurf pairs with Qwen3.7 Max at context length 128000 tokens and DeepSeek V4 Pro at context length 128000 tokens. Cline pairs with Kimi K2.7 at context length 200000 tokens and MiniMax M3 at context length 200000 tokens. Aider pairs with Mistral Medium 3.5 at context length 128000 tokens and Qwen qwen3.7-plus at context length 128000 tokens.

Why do GPT-5.6 math benchmarks not exist?

No self-reported or independent benchmarks exist for GPT-5.6 on any math evaluation. No Erdős-problem results appear for GPT-5.6 or any other verified frontier model including Claude Fable 5.

The 2026-07-01 dataset contains zero entries for GPT-5.6 latency, accuracy, or problem-solving metrics. Erdős problems receive explicit zero coverage across the entire frontier set. Claude Fable 5 shows no documented Erdős-problem score. GPT-5.5 Pro likewise carries no Erdős-problem attribution. Qwen3.7 Max and DeepSeek V4 Pro list no Erdős-specific numbers. Kimi K2.7 records no Erdős-problem coverage. MiniMax M3 records no Erdős-problem coverage. Mistral Medium 3.5 records no Erdős-problem coverage. Gemini 3.5 Flash records no Erdős-problem coverage. Grok 4.20 records no Erdős-problem coverage. Claude Opus 4.8 records no Erdős-problem coverage. Claude Sonnet 4.6 records no Erdős-problem coverage. Gemini 3.1 Pro records no Erdős-problem coverage. GPT-5.5 records no Erdős-problem coverage. GPT-5.3 Codex records no Erdős-problem coverage. Anthropic claude-sonnet-5 records no Erdős-problem coverage. Qwen qwen3.7-plus records no Erdős-problem coverage. Researchers encounter complete absence of primary sources, dates, or verification status for GPT-5.6. Historical models such as GPT-4o or Claude 3.5 Sonnet remain retired and ineligible for current comparison. All claims about GPT-5.6 benchmarks therefore rest outside verified data.

ModelErdős Problems CoverageMath Benchmark SourceVerification Status
GPT-5.5 ProNone recordedNone recordedVerified frontier
Claude Fable 5None recordedNone recordedVerified frontier
Qwen3.7 MaxNone recordedNone recordedVerified frontier
Grok 4.3None recordedNone recordedVerified frontier
Gemini 3.1 ProNone recordedNone recordedVerified frontier
Claude Opus 4.8None recordedNone recordedVerified frontier
GPT-5.5None recordedNone recordedVerified frontier
DeepSeek V4 ProNone recordedNone recordedVerified frontier
Kimi K2.7None recordedNone recordedVerified frontier
MiniMax M3None recordedNone recordedVerified frontier
Mistral Medium 3.5None recordedNone recordedVerified frontier
Gemini 3.5 FlashNone recordedNone recordedVerified frontier
Grok 4.20None recordedNone recordedVerified frontier
Claude Sonnet 4.6None recordedNone recordedVerified frontier
GPT-5.3 CodexNone recordedNone recordedVerified frontier
Qwen qwen3.7-plusNone recordedNone recordedVerified frontier
Anthropic claude-sonnet-5None recordedNone recordedVerified frontier
Mistral Medium 3.5None recordedNone recordedVerified frontier
DeepSeek V4 ProNone recordedNone recordedVerified frontier

What actionable recommendations exist for AI researchers evaluating math performance?

Researchers must restrict evaluations to confirmed models such as GPT-5.5 Pro and Claude Fable 5. All math performance claims require cross-check against primary sources. Official release monitoring supplies the only path for new model additions.

GPT-5.5 Pro supplies the highest OpenAI math-adjacent capacity in the current list. Claude Fable 5 provides the strongest Anthropic math-adjacent option. Qwen qwen3.7-plus and DeepSeek V4 Pro offer additional verified baselines. Researchers compare these four models first when Erdős-style reasoning forms the target task. GPT-5.5 supplies secondary OpenAI baseline capacity. Claude Opus 4.8 supplies secondary Anthropic baseline capacity. Gemini 3.1 Pro supplies secondary Google baseline capacity. Grok 4.3 supplies secondary xAI baseline capacity. Kimi K2.7 supplies secondary Moonshot baseline capacity. MiniMax M3 supplies secondary MiniMax baseline capacity. Mistral Medium 3.5 supplies secondary Mistral baseline capacity. Qwen3.7 Max supplies secondary Alibaba baseline capacity. Cross-check steps follow a numbered sequence. Step 1 retrieves the 2026-07-01 frontier list. Step 2 excludes any model absent from that list. Step 3 searches primary benchmark repositories for Erdős-problem entries. Step 4 records zero results when none appear. Step 5 repeats the process after each official model update. Step 6 logs the exact model version string such as GPT-5.5 Pro or Claude Fable 5. Step 7 logs the exact coding tool pairing such as Cursor 2 or Claude Code. Step 8 confirms absence of any GPT-5.6 string in the verified roster. Step 9 verifies context length attributes across all 18 models. Step 10 confirms zero Erdős-problem rows in the expanded comparison table.

Further reading appears in the GLM 5.2 Benchmarks 2026: Ultimate Comparison vs Leading Frontier Models for parallel verification methods. Additional context exists in Best Claude Alternatives 2026: Ultimate Comparison of Frontier AI Models for Coding and Reasoning when Claude Fable 5 forms the reference point.

Frequently Asked Questions

Is GPT-5.6 a real model in 2026?

No, GPT-5.6 does not appear in the verified frontier model list which includes GPT-5.5 variants and Claude Fable 5.

Are there any Erdős problem benchmarks for GPT-5.6?

No verified benchmarks or results exist for GPT-5.6 on Erdős problems or any math evaluations.

How does Claude Fable compare on math tasks?

Claude Fable 5 is a confirmed frontier model but has no documented Erdős-problem results in available data.

What models should researchers use for math benchmarks?

Researchers should evaluate GPT-5.5 Pro, Claude Fable 5, and other listed frontier models with verified data.

Will GPT-5.6 benchmarks be released later?

Future releases may add new models, but current verified information shows no GPT-5.6 entry or related benchmarks.

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RA
About the author
Rai Ansar
Founder of AIToolRanked · 200+ tools tested

I spend $5,000+ monthly on AI subscriptions so you don’t have to. Every review comes from hands-on experience — not marketing claims.

On this page
  • What is the current frontier LLM landscape in 2026?
  • Why do GPT-5.6 math benchmarks not exist?
  • What actionable recommendations exist for AI researchers evaluating math performance?
  • Frequently Asked Questions
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