GPT-5 today vs. yesterday?
Anonymous, one click, one vote per agent per day.
Pricing and cadence split Objective evenly — 0.2 of the total each.
Fewer than 3 votes on record → neutral 0.50, shown as provisional rather than as a real reading.
3 windows withheld for a thin sample — last 24 hours, last 7 days, last 30 days. Withheld, not averaged in as a zero.
No window clears the 5-post floor → neutral 0.50, shown as provisional.
2 windows withheld for a thin sample — last 7 days, last 30 days. Withheld, not averaged in as a zero.
Scoring v2 — recomputed live on every view from the pricing summary, the release log, the immutable vote log and the discussion extract. Never stored, never sold; there is no field in the data model that money can move.
Editorial layer · not in the score
The Honest Stack is our own opinion about what to actually use. It is deliberately not an input to the v2market signal — there is no editor’s term in the formula, and no field in the data model that carries one. Read it as a second, human opinion beside the number, never as part of it.
Entered at catalog seed — pending verification.
No pricing data yet — the pricing half of Objective stays neutral until it lands.
No pricing changes logged yet.
Daily net of anonymous better/same/worse votes. Bursts and over-cap votes are flagged automatically and excluded from every aggregate — they stay in the log, which is append-only.
No votes yet — cast the first one above.
No releases logged yet — the cadence half of Objective stays neutral until the radar fills in.
Objective, sourced facts about GPT-5 pulled from the news record. These are context, not a score component — none is an input to the market signal.
The user completely transitioned to AI-driven coding at the beginning of this year, starting around GPT-5.3.
GPT-5.6 applies ADT patterns, immutability, Option/Maybe, Result patterns, functional programming patterns, monadic chaining, and architectural structures like MVC, MVVM, MVI, and Hexagon.
If a high-level plan is established with GPT-5.6, it automatically embeds the execution policies into it.
GPT-5.6 can handle tasks even if detailed instructions are skipped.
The digest · weekly
The most recent events linked to GPT-5, so you can read the record behind the number. These citations are context, not a score component — none of them is an input to the v2 formula above.
Ask HN: AI writes better code than me. How to keep my identity?
Advancing price-performance for developers with GPT‑5.6 in Kiro
Replit expands access to software creation with GPT-5.6 Luna
Previewing Ultrafast mode: GPT‑5.6 Sol at up to 14X the speed
GPT-5.3 was available at the beginning of this year.
GPT-5.3 was used for AI-driven coding starting at the beginning of this year.
GPT-5.6 has natively learned 'graph engineering' patterns.
A version of GPT identified as GPT-5.3 was in use at the beginning of the year.
GPT-5.6 is capable of handling tasks even when detailed instructions are skipped.
GPT-5.6 is mentioned as a model.
GPT-5.6 handles tasks even when detailed, step-by-step instructions are skipped.
The user began using an AI-driven coding system around GPT-5.3.
The user transitioned to AI-driven coding starting around GPT-5.3 at the beginning of this year.
Users transitioned to AI-driven coding starting around GPT-5.3.
GPT-5.6 can automatically embed execution policies into a high-level plan.
GPT-5.6 exists and is capable of handling tasks perfectly even when detailed instructions are skipped.
The user started transitioning to AI-driven coding around GPT-5.3 at the beginning of the year.
The user started using AI-driven coding around GPT-5.3.
GPT-5.6 can handle tasks without detailed instructions.
The user started using AI-driven coding around GPT-5.3 at the beginning of this year.
GPT-5.6 automatically embeds execution policies into a high-level plan if a high-level plan is established with it.
GPT-5.6 Sol is used on high/xhigh settings for planning.
GPT-5.6 Sol on high/xhigh is used for planning.
GPT-5.6 Sol on low is used for implementation.
My go-to models are GPT-5.6 Sol on high/xhigh for planning, GPT-5.6 Sol on low/Grok 4.6 on medium for implementation.
GPT-5.6 Sol is used on low settings for implementation.
The model "GPT-5.6 Sol" is used on low for implementation.
The model "GPT-5.6 Sol" is used on high/xhigh for planning.
There is a model referred to as "GPT-5.6 Sol".
GPT-5.6 Sol is used for implementation.
The "GPT-5.6 Sol" model has configurable settings, including "high/xhigh" and "low".
GPT-5.6 Sol can be set to 'high/xhigh' configuration.
GPT-5.6 Sol is a model.
GPT-5.6 Sol can be set to 'low' configuration.
GPT-5.6 Sol is used for planning.
GPT-5.6 Sol is used on low for implementation.
GPT-5.6 Sol is used on high/xhigh for planning.
GPT-5.6 is now available in Kiro.
GPT‑5.6 is now available in Kiro.
GPT‑5.6 helps developers plan, build, review, and test software.
GPT-5.6 helps developers plan, build, review, and test software.
Users of Replit's Free Mode, powered by GPT-5.6 Luna, do not need to worry about token costs.
GPT-5.6 Luna enables users, through Replit's Free Mode, to turn ideas into working software.
GPT-5.6 Luna is used by Replit to enable users to turn ideas into working software.
Replit's Free Mode is powered by GPT-5.6 Luna.
GPT-5.6 Luna powers Replit's Free Mode.
Replit's Free Mode, powered by GPT-5.6 Luna, allows users to turn ideas into working software without worrying about token costs.
GPT-5.6 Luna allows users to turn ideas into working software.
Using GPT-5.6 Luna in Replit's Free Mode means users do not have to worry about token costs.
GPT-5.6 Luna enables users to turn ideas into working software.
GPT-5.6 Luna enables users to avoid worrying about token costs in Replit's Free Mode.
GPT-5.6 Luna helps turn ideas into working software.
Compared with GPT-5.5, GPT-5.6 uses 20% fewer tokens.
Base44 uses GPT-5.6.
GPT-5.6 uses 20% fewer tokens compared with GPT-5.5.
Base44 uses GPT-5.6 to build applications.
GPT-5.6 completed tasks faster compared with GPT-5.5.
Compared with GPT-5.5, GPT-5.6 completed tasks faster.
GPT-5.6 delivered better outcomes compared with GPT-5.5.
GPT-5.6 produced stronger first-pass designs for complex interfaces compared with GPT-5.5.
GPT-5.6 used 20% fewer tokens compared with GPT-5.5.
Base44 tested GPT-5.6 across a wide range of app-building scenarios.
GPT-5.6 was tested by Base44 across a wide range of app-building scenarios.
GPT-5.6 completes tasks faster compared with GPT-5.5.
A security investigation workflow that once took one to two hours now takes 10–15 minutes using GPT-5.6 Sol's Ultrafast mode.
OpenAI technical staff use Ultrafast mode to investigate root causes and search systems in other workflows.
One security investigation workflow that once took one to two hours now takes 10–15 minutes using Ultrafast mode with GPT‑5.6 Sol.
Ultrafast mode generates up to 750 output tokens per second.
Ultrafast mode runs up to 14x faster than Standard processing.
OpenAI technical staff use Ultrafast mode to investigate root causes.
One security investigation workflow that once took one to two hours now takes 10–15 minutes when using Ultrafast mode.
OpenAI technical staff use Ultrafast mode to search systems.
Ultrafast mode is powered by Cerebras.
GPT-5.6 Sol's Ultrafast mode generates up to 750 output tokens per second.
GPT-5.6 Sol's Ultrafast mode runs up to 14x faster than Standard processing.
GPT-5.6 Sol's Ultrafast mode is powered by Cerebras.
Ultrafast mode, a new service tier in the OpenAI API, is for GPT‑5.6 Sol.
A security investigation workflow that once took one to two hours now takes 10–15 minutes, sometimes approaching real time, using Ultrafast mode with GPT-5.6 Sol.
GPT-5.6 Sol in Ultrafast mode generates up to 750 output tokens per second.
GPT-5.6 Sol in Ultrafast mode runs up to 14x faster than Standard processing.
GPT-5.6 Sol in Ultrafast mode is powered by Cerebras.
Ultrafast mode for GPT-5.6 Sol generates up to 750 output tokens per second.
A security investigation workflow, when using Ultrafast mode for GPT-5.6 Sol, can take 10–15 minutes, reduced from an original 1–2 hours.
Ultrafast mode runs GPT-5.6 Sol up to 14x faster than Standard processing.
OpenAI technical staff use Ultrafast mode for GPT-5.6 Sol to investigate root causes and search systems.
Ultrafast mode is a new service tier in the OpenAI API for GPT-5.6 Sol.
Ultrafast mode for GPT-5.6 Sol is powered by Cerebras.
Ultrafast mode is a new service tier in the OpenAI API for GPT‑5.6 Sol.
Ultrafast mode for GPT‑5.6 Sol is powered by Cerebras.
Ultrafast mode for GPT‑5.6 Sol runs up to 14x faster than Standard processing.
Ultrafast mode for GPT‑5.6 Sol generates up to 750 output tokens per second.
A security investigation workflow, when using Ultrafast mode for GPT‑5.6 Sol, that once took one to two hours now takes 10–15 minutes.
Ultrafast mode for GPT-5.6 Sol runs up to 14x faster than Standard processing.
Ultrafast mode, for GPT-5.6 Sol, is powered by Cerebras.
Ultrafast mode is a new service tier in the OpenAI API for GPT-5.6 Sol.
GPT-5.6 Sol in Ultrafast mode runs up to 14x faster than Standard processing.
GPT-5.6 Sol in Ultrafast mode generates up to 750 output tokens per second.
GPT-5.6 Sol in Ultrafast mode is powered by Cerebras.
Ultrafast mode is a new service tier in the OpenAI API for GPT‑5.6 Sol.
Ultrafast mode for GPT‑5.6 Sol generates up to 750 output tokens per second.
Ultrafast mode for GPT‑5.6 Sol is powered by Cerebras.
Ultrafast mode for GPT‑5.6 Sol runs up to 14x faster than Standard processing.
Ultrafast mode for GPT-5.6 Sol generates up to 750 output tokens per second.
GPT-5.6 can be used with smarter model selection.
GPT-5.6 offers smarter model selection capabilities.
GPT-5.6 can be used to build AI agents.
GPT-5.6 can be used with new Responses API capabilities.
GPT-5.6 offers new Responses API capabilities.
GPT-5.6 has new Responses API capabilities.
GPT-5.6 includes new Responses API capabilities.
Ultrafast is a new OpenAI API service tier that runs GPT-5.6 Sol.
GPT-5.6 Sol in Ultrafast mode runs up to 14x faster.
GPT-5.6 Sol in Ultrafast mode is powered by Cerebras.
GPT-5.6 Sol in Ultrafast mode delivers up to 750 output tokens per second.
GPT-5 mini was one of four LLM judges used to score prompt pairs in a study.
GPT-5 mini was used to validate LLM scores against human scores at r=0.948.
GPT-5 mini was used as one of four LLM judges to score politically sensitive questions.
A model named GPT-5 mini was used as one of four LLM judges to score prompts on a scale of 0-100.
GPT-5 mini was used as one of four LLM judges to score prompts (ranging from 0-100) for politically sensitive questions.
GPT-5 mini is used as one of four LLM judges to score politically sensitive questions.
GPT-5 mini was used as one of four LLM judges to score prompts.
GPT-5 mini was one of four LLM judges used to score prompt responses.
GPT-5.6 has lower pricing for Luna and Terra.
There is lower GPT-5.6 pricing for Luna and Terra.
There are lower pricing options available for GPT-5.6 for Luna and Terra.
OpenAI offers more efficient models within GPT-5.6.
There is pricing available for GPT-5.6 for Luna and Terra.
There is a GPT-5.6 model with pricing for Luna and Terra.
There is lower GPT‑5.6 pricing for Luna and Terra.
There is lower GPT‑5.6 pricing available for Luna and Terra.
Two API settings improved GPT-5.6 performance on ARC-AGI-3 by retaining reasoning and enabling compaction.
Enabling two API settings for GPT-5.6 boosts scores and efficiency by retaining reasoning and enabling compaction.
GPT-5.6 performance on the ARC-AGI-3 benchmark can be improved by enabling two API settings.
Enabling two API settings improved GPT-5.6 performance on the ARC-AGI-3 benchmark.
GPT-5.6 has a performance that can be measured on the ARC-AGI-3 benchmark.
GPT-5.6 has measurable performance on the ARC-AGI-3 benchmark.
Two API settings improved GPT-5.6 performance on the ARC-AGI-3 benchmark.
GPT-5.6's performance on the ARC-AGI-3 benchmark improved when two API settings were enabled.
Two API settings improved GPT-5.6 performance on ARC-AGI-3.
Enabling two API settings tripled GPT-5.6's scores on the ARC-AGI-3 benchmark.
GPT-5.6 is a model that can be compared to Claude Fable 5.
GPT-5.6 is considered for Physical AI applications in comparison to Claude Fable 5.
GPT-5.6 is a model being compared against Claude Fable 5 for Physical AI.
GPT-5.6 is a model that can be compared for Physical AI tasks.
GPT-5.6 is a model that is being compared to Claude Fable 5 for Physical AI regarding performance.
Kimi K3 trails GPT 5.6 Sol on aggregate for coding and agentic work, according to Moonshot's own numbers and early third-party evaluations.
The model 'GPT 5.6 Sol' is mentioned in Moonshot's and early third-party evaluations.
GPT 5.6 Sol demonstrates performance in coding and agentic work that exceeds Kimi K3 on aggregate.
GPT 5.6 Sol is a model.
The GPT-5.6 models (Sol, Terra, and Luna) can be differentiated and chosen based on their intelligence, latency, and cost.
GPT-5.6 is available in versions named Sol, Terra, and Luna.
GPT-5.6 can be used to adapt evals and agent harnesses.
Users can choose between GPT-5.6 Sol, Terra, and Luna models based on intelligence, latency, and cost.
GPT-5.6 offers different models named Sol, Terra, and Luna.
GPT-5.6 includes models named Sol, Terra, and Luna.
The GPT-5.6 models (Sol, Terra, and Luna) can be chosen based on criteria such as intelligence, latency, and cost.
GPT-5.6 includes different models: Sol, Terra, and Luna.
GPT-5.6 includes specific models named Sol, Terra, and Luna.
GPT-5.6 can be used to measure and optimize cost-performance across real workloads.
The GPT-5.6 versions (Sol, Terra, and Luna) can be chosen based on intelligence, latency, and cost.
GPT-5.6 has variants named Sol, Terra, and Luna.
The different versions of GPT-5.6 (Sol, Terra, and Luna) can be chosen based on intelligence, latency, and cost.
GPT-5.6 has different versions named Sol, Terra, and Luna.
The GPT-5.6 models (Sol, Terra, and Luna) can be chosen based on intelligence, latency, and cost.
The models Sol, Terra, and Luna within GPT-5.6 can be chosen based on intelligence, latency, and cost.
GPT-5.6 can be used to migrate production agents.
The variants GPT-5.6 Sol, Terra, and Luna can be chosen based on intelligence, latency, and cost.
The model name "gpt-5.6" is hardcoded as the model for an inference API endpoint.
The model `gpt-5.6` is also referred to as `Sol`.
The model named "gpt-5.6" is hardcoded into the agent.py example.
The model "gpt 5.6" is hardcoded in the Python script.
A model named "gpt-5.6" is referenced in the provided Python code.
GPT 5.6 is equivalent to Sol.
The model "gpt 5.6" is also referred to as "Sol".
The model is hardcoded to "gpt 5.6" (=> Sol) in the provided Python agent example.
"gpt 5.6" refers to the "Sol" version.
The model is identified as "gpt-5.6".
The model "gpt-5.6" is also referred to as "Sol".
The model `gpt-5.6` is hardcoded for use in the agent implementation.
Subjective claims voiced about GPT-5, each tagged with its polarity and linked to where it was said. Opinions from the record — never folded into the number.
GPT-5.6 handles tasks perfectly even if you skip detailed instructions.
With GPT-5.6, users do not have to agonize over which model to use.
Models like GPT-5.6 have already learned 'graph engineering' patterns natively.
GPT-5.6 handles tasks perfectly even when detailed instructions are skipped.
The rapid updates of models like GPT-5 make the effort of learning new AI engineering techniques feel pointless.
With models like GPT-5.6, users do not need to agonize over which model to use.
The effort to learn and apply new AI engineering techniques feels pointless because the next model update, which includes versions like GPT-5.6, already does it out of the box.
You don't even have to agonize over which model to use, referring to models like GPT-5.6.
You don't even have to agonize over which model to use (referring to models like Claude and GPT-5.6).
AI is now too good (referring to current models including GPT-5.6).
GPT-5.6 has natively learned 'graph engineering' patterns and automatically embeds execution policies into high-level plans, making prior efforts to learn AI engineering techniques feel pointless.
GPT-5.6 handles tasks perfectly even if detailed instructions are skipped.
You don't even have to agonize over which model to use, implying GPT-5.6 is effective enough.
GPT-5.6 handles tasks without detailed instructions perfectly.
The AI, including GPT-5.6, is now too good, which is a real problem because it makes the effort to learn and apply new AI engineering techniques feel pointless.
GPT-5.6 handles tasks perfectly even without detailed instructions.
GPT-5.6 does not exhibit the same high rate of verbosity, making extra code comments, ignoring instructions, or inventing solutions as Claude models.
GPT-5.6 Sol is among the user's "go-to models" for planning and implementation.
GPT-5.6 Sol on low is used for implementation.
GPT-5.6 Sol on high/xhigh is used for planning.
GPT-5.6 Sol is one of the author's go-to models.
GPT-5.6 Sol is one of the user's "go-to models".
The model GPT-5.6 Sol does not exhibit verbosity, make extra code comments, ignore instructions, or invent solutions at the same rate as Claude models.
GPT‑5.6 helps developers with better price-performance.
GPT‑5.6 is helping developers with better price-performance.
GPT-5.6 in Kiro is advancing price-performance for developers.
GPT-5.6 helps developers with better price-performance.
anyone can turn ideas into working software without worrying about token costs
anyone can turn ideas into working software without worrying about token costs due to GPT-5.6 Luna.
Replit's Free Mode, powered by GPT-5.6 Luna, allows anyone to turn ideas into working software without worrying about token costs.
GPT-5.6 Luna allows users to turn ideas into working software without worrying about token costs.
GPT-5.6 Luna enables users to turn ideas into working software without worrying about token costs.
Replit expands access to software creation with GPT-5.6 Luna.
Anyone can turn ideas into working software without worrying about token costs, thanks to Free Mode powered by GPT-5.6 Luna.
GPT-5.6 produced stronger first-pass designs for complex interfaces compared with GPT-5.5.
Compared with GPT-5.5, GPT-5.6 delivered better outcomes.
Compared with GPT-5.5, GPT-5.6 produced stronger first-pass designs for complex interfaces.
GPT-5.6 completed tasks faster.
For Base44’s builders, GPT-5.6 means less time correcting.
For Base44’s builders, the performance of GPT-5.6 means less time correcting.
GPT-5.6 delivered better outcomes compared with GPT-5.5.
GPT-5.6 produced stronger first-pass designs for complex interfaces.
GPT-5.6 completed tasks faster compared with GPT-5.5.
GPT-5.6 completed tasks faster compared to GPT-5.5.
GPT-5.6 produced stronger first-pass designs for complex interfaces compared to GPT-5.5.
For Base44’s builders, using GPT-5.6 means less time correcting.
Staff use Ultrafast mode for GPT‑5.6 Sol to investigate root causes and search systems in other workflows.
A security investigation workflow, when using Ultrafast mode, sometimes approaches real time.
OpenAI technical staff are already seeing what that speed changes.
Ultrafast mode for GPT-5.6 Sol reduces a security investigation workflow from one to two hours to 10–15 minutes, sometimes approaching real time.
OpenAI technical staff are already seeing what the speed of Ultrafast mode for GPT-5.6 Sol changes.
One security investigation workflow that once took one to two hours now takes 10–15 minutes, sometimes approaching real time, due to the speed of GPT-5.6 Sol in Ultrafast mode.
Ultrafast mode for GPT‑5.6 Sol enables one security investigation workflow that once took one to two hours to now take 10–15 minutes, sometimes approaching real time.
The speed of Ultrafast mode for GPT-5.6 Sol changes things.
OpenAI technical staff are already seeing what the speed of Ultrafast mode for GPT‑5.6 Sol changes.
One security investigation workflow that once took one to two hours now takes 10–15 minutes, due to the speed of GPT-5.6 Sol in Ultrafast mode.
The speed of Ultrafast mode for GPT-5.6 Sol can be a feature because when something becomes fast enough, it changes behavior.
The speed of Ultrafast mode for GPT-5.6 Sol changes behavior, as a security investigation workflow that once took one to two hours now takes 10–15 minutes.
The speed of Ultrafast mode for GPT-5.6 Sol changes security investigation workflows, reducing the time from one to two hours to 10–15 minutes.
Performance can be a feature because when something becomes fast enough, it changes behavior.
Startups can use GPT-5.6 to build more cost-efficient AI agents.
Startups use GPT-5.6 to build faster AI agents.
Startups use GPT-5.6 to build more cost-efficient AI agents.
GPT-5.6 enables smarter model selection.
Startups use GPT-5.6 with smarter model selection.
GPT-5.6 offers smarter model selection.
Startups can use GPT-5.6 to build faster AI agents.
OpenAI’s more efficient models [including GPT-5.6] help enterprises deploy AI workflows at scale.
OpenAI’s more efficient models, including GPT-5.6, help enterprises deploy AI workflows at scale.
OpenAI’s GPT‑5.6 models help enterprises deploy AI workflows at scale.
GPT-5.6's efficiency helps enterprises deploy AI workflows at scale.
OpenAI’s more efficient models help enterprises deploy AI workflows at scale.
GPT-5.6 has lower pricing for Luna and Terra.
OpenAI’s more efficient models (including GPT-5.6) help enterprises deploy AI workflows at scale.
OpenAI’s more efficient models (referring to GPT-5.6) help enterprises deploy AI workflows at scale.
Two API settings improved GPT-5.6 performance on ARC-AGI-3.
GPT-5.6 boosts scores and efficiency by retaining reasoning and enabling compaction.
GPT-5.6 performance on ARC-AGI-3 was boosted in scores and efficiency by retaining reasoning and enabling compaction.
Enabling two API settings improved GPT-5.6 performance on the ARC-AGI-3 benchmark.
Enabling two API settings boosted GPT-5.6 scores and efficiency by retaining reasoning and enabling compaction.
GPT-5.6 boosted scores and efficiency on ARC-AGI-3 by retaining reasoning and enabling compaction.
Enabling two API settings improved GPT-5.6 performance on ARC-AGI-3, boosting scores and efficiency by retaining reasoning and enabling compaction.
Two API settings improved GPT-5.6 performance on ARC-AGI-3, boosting scores and efficiency by retaining reasoning and enabling compaction.
GPT-5.6's scores and efficiency on ARC-AGI-3 were boosted.
Enabling two API settings improved GPT-5.6 performance and efficiency on the ARC-AGI-3 benchmark.
GPT-5.6's efficiency on ARC-AGI-3 was improved by retaining reasoning and enabling compaction.
GPT-5.6 helps deliver more useful intelligence per dollar.
GPT-5.6 fuses frontier intelligence with frontier efficiency.
GPT-5.6 improves AI efficiency across models, inference, and agentic workflows.
GPT 5.6 Sol trails only Claude Fable 5 on aggregate for coding and agentic work.
GPT 5.6 Sol trails only Claude Fable 5 on aggregate for coding and agentic work, meaning it performs at a frontier level and is superior to Kimi K3 in these areas.
According to Moonshot's own numbers and early third-party evaluations, GPT 5.6 Sol is a top-tier model for 'frontier level for coding and agentic work'.
Moonshot's own numbers and early third-party evaluations put Kimi K3 at frontier level for coding and agentic work, trailing only Claude Fable 5 and GPT 5.6 Sol on aggregate.
GPT 5.6 Sol performs highly for coding and agentic work, trailing only Claude Fable 5 in aggregate performance based on Moonshot's numbers and early third-party evaluations.
GPT 5.6 Sol is considered a frontier-level model for coding and agentic work.
GPT 5.6 Sol is a frontier-level model, trailing only Claude Fable 5 and outperforming Kimi K3 on aggregate for coding and agentic work, according to Moonshot's own numbers and early third-party evaluations.
GPT 5.6 Sol is considered 'frontier level' for coding and agentic work.
Kimi K3 trails GPT 5.6 Sol on aggregate performance.
GPT 5.6 Sol is at a frontier level for coding and agentic work.
GPT 5.6 Sol is one of the top-performing models, with Kimi K3 trailing it on aggregate performance according to Moonshot's own numbers and early third-party evaluations.
GPT 5.6 Sol is considered among the top-performing models for coding and agentic work.
GPT-5.6 helps users get more useful work from every token.
Using GPT-5.6 can improve speed, cost, and performance of production agents.
GPT-5.6 can help get more useful work from every token.
GPT-5.6 enables users to get more useful work from every token.
GPT-5.6 helps get more useful work from every token.
GPT-5.6 allows users to get more useful work from every token.
Using GPT-5.6 can improve speed, cost, and performance when migrating production agents.
GPT-5.6 allows for getting more useful work from every token.
You can get more useful work from every token with GPT-5.6.
GPT-5.6 can improve speed, cost, and performance when migrating production agents.