GPT-6 Astra vs GPT-6.1 Sol

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v4.4#6 of 5784.6 score · 3/3 sources · complete#5 of 5785.9 score · 3/3 sources · complete
CostLower is better · Published-token output estimate#26 of 27$0.520 per LiveBench case#15 of 27$0.104 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.9#18 of 2746.1 score · 3/3 sources · complete#5 of 2760.1 score · 3/3 sources · complete
Pricing and Limits
Input priceFrom · USD / 1M tokens$10.00Openai ↗ · Sep 4, 2026$2.00Openai ↗ · Sep 29, 2026
Output priceFrom · USD / 1M tokens$50.00Openai ↗ · Sep 4, 2026$10.00Openai ↗ · Sep 29, 2026
Context windowMaximum documented tokens1,050K1,050K
Model facts checkedSep 4, 2026View model evidence →Sep 29, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
BenchmarkGPT-6 AstraGPT-6.1 Sol
ARC-AGI-1verified-v1-613c385b0c93 · verified_score · statistical tie98.50100% of row best · percent · GPT-6 Astra (XHigh)98.50100% of row best · percent · GPT-6.1 Sol (XHigh)
ARC-AGI-2verified-v2-be40992860f9 · verified_score · leader95.00100% of row best · percent · GPT-6 Astra (Max)94.1799% of row best · percent · GPT-6.1 Sol (Max)
LMArena Search Arenasearch-2026-10-13-a64ca3b12b6e · arena_rating · statistical tie1,233.62100% of row best · rating · gpt-6-astra-max; 95% CI [1225.77363708, 1241.45798456]; votes 18483; rank 71,233.10100% of row best · rating · gpt-6.1-sol-max; 95% CI [1224.24145466, 1241.96324521]; votes 5555; rank 8
LMArena Text Arenatext-2026-10-15-a64ca3b12b6e · arena_rating · statistical tie1,442.69100% of row best · rating · gpt-6-astra-max; 95% CI [1436.40189535, 1448.98654490]; votes 12163; rank 691,447.67100% of row best · rating · gpt-6.1-sol-max; 95% CI [1439.59979847, 1455.74908604]; votes 6320; rank 57
LMArena Vision Arenavision-2026-10-09-a64ca3b12b6e · arena_rating · statistical tie1,281.3599% of row best · rating · gpt-6-astra-max; 95% CI [1270.63592513, 1292.07239183]; votes 3590; rank 361,288.19100% of row best · rating · gpt-6.1-sol-max; 95% CI [1273.83724449, 1302.54208160]; votes 1813; rank 30
LiveBench2026-06-25-90f8d44da965 · overall · leader86.47100% of row best · percent · gpt-6-astra-max · 10,407 output tokens / case86.14100% of row best · percent · gpt-6.1-sol-max · 10,375 output tokens / case
ToneBench2026-10-17-10-task-e9c76358afb2 · overall_score · unverified82.32100% of row best · points · GPT-6 Astra (max) · 20,449 output tokens / case82.44100% of row best · points · GPT-6.1 Sol (max) · 21,221 output tokens / case
Overall ResultCounted from the protocol-matched rows above · 4 ties2 benchmark winsOverall lead0 benchmark wins

Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.

Quality Versus Estimated Output Cost

Full ranking →
Efficiency FrontierLiveBench overall · output estimate
Upper-left is better
xAIZ.aiOpenAIMiniMaxDeepSeekMistral AIQwenGoogle DeepMindMoonshot AIAnthropic
LiveBench quality versus score-adjusted output costEach dot is a reviewed major-model configuration and is colored by developer. Higher means a better LiveBench overall score. Farther left means lower estimated output cost after adjusting by the score. A dotted line connects the non-dominated frontier observations. When models are selected, their sourced families remain prominent, unrelated observations retain their developer colors at lower opacity, and an orange ring identifies each selected model.$0.0050$0.010$0.050$0.100$0.500$1.006873798489Grok Build 0.1GLM 5.3 FlashGPT-6 Luna (max)DeepSeek V4.1 Flash (max)GPT-6.1 Sol (max)GPT-6 Astra (max)Claude Fable 5.1Score-adjusted output cost per LiveBench case (log) →LiveBench overall →
The dotted frontier connects measured, non-dominated major-model observations. With a selection, sourced families stay prominent, unrelated observations retain their developer colors at lower opacity, and orange rings mark the selected model or models. Family lines connect models only when their sourced family and generation match. Cost is estimated from published output tokens and the lowest current USD output rate; it excludes input, caching, batch discounts, and provider-specific benchmark execution details.

Side-by-Side Facts

FieldGPT-6 AstraGPT-6.1 Sol
DeveloperOpenAIOpenAI
FamilyGpt 6Gpt 6
ModelGPT-6 AstraGPT-6.1 Sol
VersionGPT-6 AstraGPT-6.1 Sol
Lifecycleactiveactive
Released2026-09-032026-09-29
Knowledge cutoff2026-04-302026-04-30
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window1,050K1,050K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessOpenai (Standard), Openrouter (Standard)Openai (Standard), Openrouter (Standard)
Capabilitieschat, computer-use, generation, reasoning, research, toolschat, computer-use, generation, reasoning, tools
Data residencyUnknownUS, EU
Default snapshotUnknowngpt-6.1-sol
Maximum input tokensUnknown922000
Multi-agentUnknownResponses API beta
Reasoning effortUnknownlow, medium, high, xhigh, max
Regional processing premiumUnknown10% where available
Supported API routesUnknownv1/responses, v1/chat/completions, v1/batch
Supported toolsUnknownweb_search, file_search, image_generation, code_interpreter, hosted_shell, apply_patch, skills, computer_use, mcp, tool_search
Tier 1 rate limitsUnknown500 RPM; 500,000 TPM
Tier 2 rate limitsUnknown5,000 RPM; 1,000,000 TPM
Tier 3 rate limitsUnknown5,000 RPM; 2,000,000 TPM
Tier 4 rate limitsUnknown10,000 RPM; 4,000,000 TPM
Tier 5 rate limitsUnknown15,000 RPM; 40,000,000 TPM
Tool calling routeUnknownResponses API only

GPT-6 Astra Capabilities

chatcomputer-usegenerationreasoningresearchtools
Serving providers2
Canonical IDopenai/gpt-6-astra

GPT-6.1 Sol Capabilities

chatcomputer-usegenerationreasoningtools
Serving providers2
Canonical IDopenai/gpt-6.1-sol

Primary Evidence

Sources and Freshness

Questions

GPT-6 Astra vs GPT-6.1 Sol FAQs

Is GPT-6 Astra or GPT-6.1 Sol better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both GPT-6 Astra and GPT-6.1 Sol, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, GPT-6 Astra or GPT-6.1 Sol?+

GPT-6 Astra is $10.00 and GPT-6.1 Sol is $2.00 per million tokens, so GPT-6.1 Sol is cheaper on this metric. GPT-6 Astra is $50.00 and GPT-6.1 Sol is $10.00 per million tokens, so GPT-6.1 Sol is cheaper on this metric.

Which has a larger context window, GPT-6 Astra or GPT-6.1 Sol?+

Both models have the same sourced context window: 1,050,000 tokens each.

Which performs better in benchmarks, GPT-6 Astra or GPT-6.1 Sol?+

GPT-6 Astra leads the current overall benchmark count. The result uses 6 protocol-matched benchmarks from 3 publishers; it is not a universal quality score.

Can GPT-6 Astra or GPT-6.1 Sol be self-hosted?+

Both models have the same sourced self-hosting status: unsupported. GPT-6 Astra is not marked open weight; GPT-6.1 Sol is not marked open weight.

Can GPT-6 Astra and GPT-6.1 Sol understand images?+

GPT-6 Astra is documented with image input; GPT-6.1 Sol is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, GPT-6 Astra or GPT-6.1 Sol?+

Both models have the same sourced maximum output: 128,000 tokens each.

Do GPT-6 Astra and GPT-6.1 Sol support reasoning and tool use?+

GPT-6 Astra: reasoning, tool calling, and image input. GPT-6.1 Sol: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, GPT-6 Astra or GPT-6.1 Sol?+

GPT-6 Astra has 2 sourced provider routes; GPT-6.1 Sol has 2, a tie.

Which offers better value, GPT-6 Astra or GPT-6.1 Sol?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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