{"id":15250,"date":"2026-06-12T00:10:40","date_gmt":"2026-06-12T04:10:40","guid":{"rendered":"https:\/\/wp.glbgpt.com\/?p=15250"},"modified":"2026-06-12T00:10:40","modified_gmt":"2026-06-12T04:10:40","slug":"claude-fable-5-vs-gpt-5-5","status":"publish","type":"post","link":"https:\/\/wp.glbgpt.com\/th\/hub\/claude-fable-5-vs-gpt-5-5","title":{"rendered":"Claude Fable 5 vs GPT-5.5: Benchmarks, Cost, and Coding Compared"},"content":{"rendered":"

\u0e42\u0e04\u0e25\u0e14 \u0e40\u0e1f\u0e40\u0e1a\u0e25 5<\/a> \u0e41\u0e25\u0e30 \u0e08\u0e35\u0e1e\u0e35\u0e17\u0e35-5.5<\/a> are two frontier AI models built for advanced reasoning, coding, research, long-context tasks, and agentic workflows.Choosing <\/strong>\u0e23\u0e30\u0e2b\u0e27\u0e48\u0e32\u0e07 Claude Fable 5 and GPT-5.5 <\/strong>is not just about picking the model with the most impressive launch demo. For most users, the real question is simpler: which model gives you better answers, faster workflows, stronger coding support, and better value for the money?<\/p>\n\n\n\n

In this comparison, we\u2019ll look at Claude Fable 5 vs GPT-5.5 across three areas people care about most: benchmarks, cost, and coding performance<\/strong>.<\/p>\n\n\n\n

Claude Fable 5 vs GPT-5.5: Quick Comparison<\/h2>\n\n\n\n
\u0e2b\u0e21\u0e27\u0e14\u0e2b\u0e21\u0e39\u0e48<\/th>\u0e42\u0e04\u0e25\u0e14 \u0e40\u0e1f\u0e40\u0e1a\u0e25 5<\/th>\u0e08\u0e35\u0e1e\u0e35\u0e17\u0e35-5.5<\/th><\/tr><\/thead>
\u0e1c\u0e39\u0e49\u0e43\u0e2b\u0e49\u0e1a\u0e23\u0e34\u0e01\u0e32\u0e23<\/td>\u0e41\u0e2d\u0e19\u0e42\u0e17\u0e23\u0e1b\u0e34\u0e01<\/td>\u0e42\u0e2d\u0e40\u0e1e\u0e48\u0e19\u0e40\u0e2d\u0e44\u0e2d<\/td><\/tr>
Release timing<\/td>June 9, 2026, according to Anthropic docs<\/td>April 23, 2026, according to OpenAI<\/td><\/tr>
API model ID<\/td>claude-fable-5<\/code><\/td>gpt-5.5<\/code><\/td><\/tr>
\u0e2b\u0e19\u0e49\u0e32\u0e15\u0e48\u0e32\u0e07\u0e1a\u0e23\u0e34\u0e1a\u0e17<\/td>1 \u0e25\u0e49\u0e32\u0e19\u0e42\u0e17\u0e40\u0e04\u0e47\u0e19<\/td>1M tokens for API; 400K in Codex<\/td><\/tr>
\u0e01\u0e33\u0e25\u0e31\u0e07\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14<\/td>Up to 128K tokens<\/td>Not directly stated in the launch article<\/td><\/tr>
API input price<\/td>$10 \/ 1M \u0e42\u0e17\u0e40\u0e04\u0e47\u0e19<\/td>$5 \/ 1M \u0e42\u0e17\u0e40\u0e04\u0e47\u0e19<\/td><\/tr>
API output price<\/td>$50 \/ 1M \u0e42\u0e17\u0e40\u0e04\u0e47\u0e19<\/td>$30 \/ 1M tokens<\/td><\/tr>
Coding focus<\/td>Long-running agents, large migrations, complex implementations<\/td>Agentic coding, debugging, tool use, Codex workflows<\/td><\/tr>
Notable safeguards<\/td>Can refuse or route certain high-risk requests to Opus 4.8<\/td>Additional safeguards noted in system card and API rollout<\/td><\/tr>
\u0e40\u0e2b\u0e21\u0e32\u0e30\u0e2a\u0e21\u0e17\u0e35\u0e48\u0e2a\u0e38\u0e14<\/td>Ambitious long-horizon work, enterprise agents, complex coding projects<\/td>Coding agents, research, tool-heavy workflows, broad productivity<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

What This Means for Users<\/h3>\n\n\n\n

Claude Fable 5 is positioned as Anthropic\u2019s highest-capability widely released model, especially for demanding reasoning and long-horizon agentic work. GPT-5.5 is positioned by OpenAI as a major step forward for coding, research, professional workflows, and computer-based tasks.<\/p>\n\n\n\n

For everyday users, the difference may not always be obvious in a simple chatbot conversation. The gap becomes clearer when the task involves larger context, multi-step coding, tool use, debugging, research, or long document processing.<\/p>\n\n\n\n

\u0e40\u0e25\u0e37\u0e2d\u0e01 \u0e42\u0e04\u0e25\u0e14 \u0e40\u0e1f\u0e40\u0e1a\u0e25 5<\/a><\/strong> if you need deep context, careful reasoning, and long-running task execution.<\/p>\n\n\n\n

\u0e40\u0e25\u0e37\u0e2d\u0e01 \u0e08\u0e35\u0e1e\u0e35\u0e17\u0e35-5.5<\/a><\/strong> if you need strong coding performance, lower token pricing, tool-heavy workflows, and broad professional productivity.<\/p>\n\n\n\n

If you want to use and compare two models yourself, \u0e42\u0e01\u0e25\u0e1a\u0e2d\u0e25 \u0e08\u0e35\u0e1e\u0e35\u0e17\u0e35<\/a><\/strong> offers an all-in-one AI workspace where you can access multiple AI models  with basic LLM plans starting at just $5.8<\/strong>.<\/p>\n\n\n\n

\"try<\/a><\/figure>\n\n\n\n
\n
Try Claude Fable 5<\/strong><\/a><\/div>\n<\/div>\n\n\n\n

Claude Fable 5 vs GPT-5.5 Benchmarks<\/h2>\n\n\n\n
\"Claude<\/figure>\n\n\n\n

Why Benchmarks Matter<\/h3>\n\n\n\n

Benchmarks help users compare model performance across coding, reasoning, math, tool use, and long-context tasks.<\/strong> They are especially useful for developers and businesses deciding which model to use in production workflows.<\/p>\n\n\n\n

However, benchmarks are not the same as real-world performance<\/em>. A model may score well on a coding benchmark but still perform differently on your codebase, framework, or prompt style.<\/p>\n\n\n\n

Official GPT-5.5 Benchmark Data<\/h3>\n\n\n\n

OpenAI has published several benchmark results for GPT-5.5.<\/a> These include coding, professional work, tool use, browsing, math, and cybersecurity evaluations.<\/p>\n\n\n\n

\u0e40\u0e01\u0e13\u0e11\u0e4c\u0e21\u0e32\u0e15\u0e23\u0e10\u0e32\u0e19<\/th>GPT-5.5 Score<\/th>What It Measures<\/th><\/tr><\/thead>
\u0e40\u0e17\u0e2d\u0e23\u0e4c\u0e21\u0e34\u0e19\u0e31\u0e25-\u0e40\u0e1a\u0e19\u0e0a\u0e4c 2.0<\/td>82.7%<\/td>Complex terminal and command-line workflows<\/td><\/tr>
SWE-Bench Pro<\/td>58.6%<\/td>Real-world GitHub issue resolution<\/td><\/tr>
OSWorld-Verified<\/td>78.7%<\/td>Operating computer environments<\/td><\/tr>
BrowseComp<\/td>84.4%<\/td>Web browsing and information retrieval<\/td><\/tr>
FrontierMath \u0e23\u0e30\u0e14\u0e31\u0e1a 1-3<\/td>51.7%<\/td>Advanced math reasoning<\/td><\/tr>
FrontierMath Tier 4<\/td>35.4%<\/td>Harder frontier math problems<\/td><\/tr>
CyberGym<\/td>81.8%<\/td>Cybersecurity task performance<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Claude Fable 5 Benchmark Notes<\/h3>\n\n\n\n

In Anthropic\u2019s official table, Claude Mythos 5 \/ Fable 5 leads GPT-5.5 on several cited benchmark categories, including SWE-Bench Pro, FrontierCode Diamond, OSWorld-Verified, AutomationBench, Blueprint-Bench 2, Legal Agent Benchmark, and HLE with tools. <\/p>\n\n\n\n

\"Claude<\/figure>\n\n\n\n

For coding-focused readers, the most relevant results are SWE-Bench Pro, FrontierCode Diamond, and Terminal-Bench 2.1,<\/strong> because these benchmarks better reflect long-horizon software engineering, terminal workflows, and agentic coding tasks<\/strong>.<\/p>\n\n\n\n

\"Claude<\/figure>\n\n\n\n

However, the benchmark data should be interpreted carefully<\/em>. Anthropic reports Claude Mythos 5 and Claude Fable 5 as a combined benchmark column, and some starred results include important safety and fallback caveats<\/strong>. For this reason, the most accurate phrasing is not \u201cClaude Fable 5 beats GPT-5.5 on every benchmark,\u201d but rather \u201cAnthropic\u2019s official Claude Mythos 5 \/ Fable 5 benchmark table reports stronger results than GPT-5.5 across several coding, reasoning, and tool-use evaluations<\/strong>.\u201d<\/p>\n\n\n\n

\"Claude<\/figure>\n\n\n\n

Claude Fable 5 vs GPT-5.5 Cost<\/h2>\n\n\n\n

Official API Pricing<\/h3>\n\n\n\n
\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07<\/th>\u0e23\u0e32\u0e04\u0e32\u0e17\u0e35\u0e48\u0e1b\u0e49\u0e2d\u0e19<\/th>\u0e23\u0e32\u0e04\u0e32\u0e02\u0e32\u0e22<\/th>Cached Input<\/th><\/tr><\/thead>
\u0e42\u0e04\u0e25\u0e14 \u0e40\u0e1f\u0e40\u0e1a\u0e25 5<\/td>$10 \/ 1M \u0e42\u0e17\u0e40\u0e04\u0e47\u0e19<\/td>$50 \/ 1M \u0e42\u0e17\u0e40\u0e04\u0e47\u0e19<\/td>Check Anthropic pricing details<\/td><\/tr>
\u0e08\u0e35\u0e1e\u0e35\u0e17\u0e35-5.5<\/td>$5 \/ 1M \u0e42\u0e17\u0e40\u0e04\u0e47\u0e19<\/td>$30 \/ 1M tokens<\/td>$0.50 \/ 1M \u0e42\u0e17\u0e40\u0e04\u0e47\u0e19<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Cost Comparison Example<\/h3>\n\n\n\n
Example Workload<\/th>\u0e42\u0e04\u0e25\u0e14 \u0e40\u0e1f\u0e40\u0e1a\u0e25 5<\/th>\u0e08\u0e35\u0e1e\u0e35\u0e17\u0e35-5.5<\/th><\/tr><\/thead>
1M input tokens<\/td>$10<\/td>$5<\/td><\/tr>
1M output tokens<\/td>$50<\/td>$30<\/td><\/tr>
1M input + 1M output<\/td>$60<\/td>$35<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Based on token pricing alone, GPT-5.5 is cheaper. Claude Fable 5 costs about 2x<\/strong> \u0e21\u0e32\u0e01\u0e02\u0e36\u0e49\u0e19<\/strong> for input and about 1.67x<\/strong> \u0e21\u0e32\u0e01\u0e02\u0e36\u0e49\u0e19<\/strong> \u0e2a\u0e33\u0e2b\u0e23\u0e31\u0e1a\u0e40\u0e2d\u0e32\u0e15\u0e4c\u0e1e\u0e38\u0e15.<\/p>\n\n\n\n

Why Real Cost Is More Than Token Price<\/h3>\n\n\n\n

Token pricing is only part of the cost. The real cost also depends on:<\/p>\n\n\n\n