{"id":2914,"date":"2026-09-04T19:42:14","date_gmt":"2026-09-04T19:42:14","guid":{"rendered":"https:\/\/dmarketertayeeb.com\/blog\/gpt-6-astra-vs-claude-fable-gemini-pro\/"},"modified":"2026-09-05T14:35:48","modified_gmt":"2026-09-05T14:35:48","slug":"gpt-6-astra-vs-claude-fable-gemini-pro","status":"publish","type":"post","link":"https:\/\/dmarketertayeeb.com\/blog\/gpt-6-astra-vs-claude-fable-gemini-pro\/","title":{"rendered":"GPT-6 Astra vs Claude Fable 5.1 and Gemini 3.1 Pro: Current Frontier Comparison"},"content":{"rendered":"\n

Short answer:<\/strong> GPT-6 Astra, Claude Fable 5.1 and Gemini 3.1 Pro Preview occupy overlapping frontier-work territory, but the providers document different interfaces, prices, evaluation sets and availability boundaries. The evidence supports a task-specific comparison\u2014not an invented universal ranking. Astra and Claude Fable 5.1 each document a 128,000-token maximum output in the linked model documentation; Gemini\u2019s preview lists 65,536. OpenAI reports strengths on several computer-use and coding tests, Anthropic reports strong Fable 5.1 results on its selected evaluations, and Google\u2019s preview exposes broader multimodal input and grounding features. Run your own fixed, representative set before switching.<\/p>\n\n\n\n

This article is a current cross-vendor comparison. The separate GPT-5.5 and GPT-5.4 comparison<\/a> covers older OpenAI compatibility and the August 31 Codex retirement. Use the named use-case guide<\/a> for build examples and the Astra pricing guide<\/a> for long-context arithmetic.<\/p>\n\n\n\n

Models and documented specifications<\/h2>\n\n\n\n
\n\n
Model<\/th>Documented positioning and interface<\/th>Context \/ max output<\/th>Published API price<\/th><\/tr><\/thead>\n
GPT-6 Astra<\/strong>
gpt-6-astra<\/code><\/td>		OpenAI: difficult reasoning, coding, computer use, research and documents; Responses tools, structured outputs, compaction and asynchronous tools.<\/td>1,050,000 \/ 128,000<\/td>$10 input; $1 cached input; $12.50 cache write; $50 output per 1M<\/td><\/tr>\n
Claude Fable 5.1<\/strong>
claude-fable-5-1<\/code><\/td>		Anthropic: demanding reasoning, long-running agents, coding and vision; tool use and image input.<\/td>1,000,000 \/ 128,000<\/td>$10 input; $50 output per MTok; cache-read pricing and regional terms apply.<\/td><\/tr>\n
Gemini 3.1 Pro Preview<\/strong>
gemini-3.1-pro-preview<\/code><\/td>		Google: complex multimodal reasoning and agentic\/coding work; text, image, video, audio and PDF input, grounding and code execution.<\/td>1,048,576 \/ 65,536<\/td>$2 input \/ $12 output up to 200K; $4 \/ $18 above 200K, per Google\u2019s pricing page.<\/td><\/tr>\n
GPT-5.6 Sol<\/strong><\/td>OpenAI\u2019s established complex professional-work baseline.<\/td>1,050,000 \/ 128,000<\/td>$4 input; $0.40 cached input; $20 output per 1M on the current model\/rate documentation.<\/td><\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n

Source the Astra row from the OpenAI model page<\/a>, Fable 5.1 from Anthropic\u2019s active model overview<\/a>, Gemini from Google\u2019s current model page<\/a> and Sol from its OpenAI model page<\/a>. Do not compare prices without checking cache, long-context, mode, thinking-token and regional rules. The quoted Gemini tiers are also listed on Google\u2019s pricing page<\/a>.<\/p>\n\n\n\n

What current benchmark evidence can and cannot show<\/h2>\n\n\n\n

OpenAI\u2019s Astra announcement<\/a> publishes vendor-reported benchmark results. The following selected rows preserve the task name, score and comparison set; blank entries mean the announcement did not publish that model\u2019s result in the row.<\/p>\n\n\n\n

\n\n
Benchmark<\/th>GPT-6 Astra<\/th>GPT-5.6 Sol<\/th>Claude Fable 5.1<\/th>What to read carefully<\/th><\/tr><\/thead>\n
OSWorld 2.0<\/td>72.6<\/td>65.7<\/td>\u2014<\/td>Computer-use evaluation reported by OpenAI.<\/td><\/tr>\n
ScreenSpot-Pro<\/td>92.7<\/td>76.9<\/td>\u2014<\/td>Screen-grounding task; not a general quality score.<\/td><\/tr>\n
AutomationBench<\/td>41.4<\/td>18.1<\/td>31.4<\/td>Professional automation task; preserve the benchmark definition.<\/td><\/tr>\n
Terminal-Bench 4<\/td>57.9<\/td>37.3<\/td>55.8<\/td>Coding\/terminal task; environment and harness matter.<\/td><\/tr>\n
DeepSWE<\/td>74.1<\/td>72.7<\/td>67.4<\/td>Software-engineering task; not a product-wide ranking.<\/td><\/tr>\n
Artificial Analysis Intelligence Index v4.1.1<\/td>61.2<\/td>60.9<\/td>65.7<\/td>Independent index quoted in OpenAI\u2019s announcement; definitions and date still matter.<\/td><\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n

These scores are attributed to OpenAI\u2019s announcement<\/strong>. They are not an independent replication, and the benchmark sets differ in task coverage, tools, scaffolding, date and safety configuration. Anthropic\u2019s Fable 5.1 announcement<\/a> also publishes its own comparison table and caveats, while Google\u2019s preview page emphasizes capabilities rather than a directly comparable universal score. A responsible conclusion is that Astra appears strong on the selected OpenAI-reported computer-use and coding rows; it is not that Astra wins every task.<\/p>\n\n\n\n

Capability differences that change implementation<\/h2>\n\n\n\n
\n\n
Requirement<\/th>Why it may favor a route<\/th>Question to test<\/th><\/tr><\/thead>\n
Text and image only, long Responses tool loop<\/td>Astra or Sol may simplify an OpenAI-native integration.<\/td>Does the tool schema, retry and structured-output contract pass?<\/td><\/tr>\n
Video, audio or PDF as direct model inputs<\/td>Gemini\u2019s current preview documentation lists these input modalities.<\/td>Do governance and quality checks support those modalities?<\/td><\/tr>\n
Long-running agent with provider-native tool use<\/td>Astra and Fable 5.1 both document agent-oriented positioning.<\/td>Who owns tool authorization, retention, and cancellation?<\/td><\/tr>\n
Search grounding or Google ecosystem context<\/td>Gemini\u2019s page lists search\/Maps grounding and URL context.<\/td>Are source attribution and regional data controls acceptable?<\/td><\/tr>\n
Lower-cost OpenAI baseline<\/td>Sol may be sufficient when the task does not need Astra\u2019s premium route.<\/td>Does Sol clear the same acceptance test?<\/td><\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n

How to run a fair evaluation<\/h2>\n\n\n\n
    \n
  1. Freeze 50\u2013200 representative cases with expected facts, tool permissions and a human rubric.<\/li>\n
  2. Give each provider the same source material and an equivalent tool contract; record where interfaces differ.<\/li>\n
  3. Measure correctness, groundedness, tool-call validity, unsafe requests, latency, retries, output length and reviewer time.<\/li>\n
  4. Price the accepted result using each provider\u2019s token classes, cache rules and mode rather than only a base input rate.<\/li>\n
  5. Report failures and abstentions as well as successful outputs. Keep an established GPT-5.6 Sol, Terra or Luna route as a baseline where appropriate.<\/li>\n<\/ol>\n\n\n\n

    The Astra safety guide<\/a> and Astra API coding guide<\/a> are part of the evaluation for computer use or cyber-sensitive work. A benchmark score does not authorize access to an ad account, production database or customer record.<\/p>\n\n\n\n

    Frequently asked questions<\/h2>\n\n\n\n

    Is GPT-6 Astra the best frontier model?<\/h3>\n\n\n\n

    No universal winner is established by the cited tables. Choose by task, interface, data boundary, evaluation result and accepted-result cost.<\/p>\n\n\n\n

    Why is Gemini\u2019s price not directly comparable?<\/h3>\n\n\n\n

    Google publishes different thresholds, cached rates and input modalities; providers count thinking, tool and long-context usage differently. Normalize a real workload.<\/p>\n\n\n\n

    Should I compare Astra to GPT-5.6 Sol?<\/h3>\n\n\n\n

    Yes, as a same-provider baseline. The older-model comparison covers GPT-5.5 and GPT-5.4 compatibility; this page focuses on cross-vendor frontier choices.<\/p>\n\n\n\n

    Bottom line<\/h2>\n\n\n\n

    Use the specifications to choose test candidates, use attributed benchmark rows to form hypotheses and use a fixed workload to make the decision. Astra\u2019s published evidence is meaningful on selected tasks, but it is not permission to invent a cross-vendor ranking or skip your own safety, quality and cost evaluation.<\/p>\n\n\n\n

    Specifications and prices are current as of September 5, 2026; model and pricing pages from OpenAI<\/a>, Anthropic<\/a> and Google<\/a> are linked above. Benchmark figures are vendor-reported.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

    Compare GPT-6 Astra with Claude Fable 5.1, Gemini 3.1 Pro Preview and GPT-5.6 Sol using current primary specs, pricing boundaries and a fair test protocol.<\/p>\n","protected":false},"author":1,"featured_media":2936,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[209,274],"tags":[365,294,393,300,421],"class_list":["post-2914","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-tools-reviews","tag-ai-benchmarks","tag-ai-model-comparison","tag-ai-model-releases","tag-ai-models","tag-multimodal-ai","has-featured-image"],"_links":{"self":[{"href":"https:\/\/dmarketertayeeb.com\/blog\/wp-json\/wp\/v2\/posts\/2914","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dmarketertayeeb.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dmarketertayeeb.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dmarketertayeeb.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/dmarketertayeeb.com\/blog\/wp-json\/wp\/v2\/comments?post=2914"}],"version-history":[{"count":1,"href":"https:\/\/dmarketertayeeb.com\/blog\/wp-json\/wp\/v2\/posts\/2914\/revisions"}],"predecessor-version":[{"id":2937,"href":"https:\/\/dmarketertayeeb.com\/blog\/wp-json\/wp\/v2\/posts\/2914\/revisions\/2937"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dmarketertayeeb.com\/blog\/wp-json\/wp\/v2\/media\/2936"}],"wp:attachment":[{"href":"https:\/\/dmarketertayeeb.com\/blog\/wp-json\/wp\/v2\/media?parent=2914"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dmarketertayeeb.com\/blog\/wp-json\/wp\/v2\/categories?post=2914"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dmarketertayeeb.com\/blog\/wp-json\/wp\/v2\/tags?post=2914"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}