GPT-6.1 Sol vs GPT-6 Astra: One Point Apart, 4.5× Cheaper per Task (2026)

The short answer: GPT-6.1 Sol delivers about 98% of GPT-6 Astra's measured intelligence at roughly 22% of the cost per task. Artificial Analysis, which runs both models through the same benchmark harness, scores them 51.8 vs 52.7 on its intelligence index — and bills $0.72 vs $3.26 per benchmark task. Official API rates are exactly one-fifth: $2 input / $10 output per million tokens against Astra's $10 / $50. Sol is also slightly faster in output (62.4 vs 58.3 tokens per second). Pick Astra when a task is expensive to get wrong; pick Sol for everything else — it is OpenAI's own recommended default for complex work.

Both models sit in the same GPT-6 generation: Astra launched September 3, 2026 as the flagship ("our most capable model for the most demanding work"), and GPT-6.1 Sol followed on September 29, 2026 ("near-Astra performance for complex work at a lower cost"). OpenAI's docs compare them to each other explicitly — Sol exists to answer "do I really need Astra?" This page works through that question with prices, benchmark data, and one workload most comparisons skip: generating motion graphics as code. If you are comparing video generation models instead, start with the model index or the head-to-head Arena.

Quick verdict

• Raw intelligence: GPT-6 Astra, by 0.9 points on the same lab's index (52.7 vs 51.8) — both in the top tier of a 227-model field.
• Cost: GPT-6.1 Sol, decisively. One-fifth the per-token rates, and $0.72 vs $3.26 per benchmark task in third-party measurement.
• Speed: GPT-6.1 Sol, slightly. 62.4 vs 58.3 output tokens per second on the same rig, plus an Ultrafast tier documented at 6× standard with US and EU residency (Astra's Ultrafast lists US residency only).
• Context and specs: a tie. Same 1.05M-token window (922K max input), same 128K output, same April 30, 2026 knowledge cutoff, same over-272K surcharge structure, same reasoning-effort ladder from low to max.
• ChatGPT access: both live in ChatGPT Work and Codex, not consumer Chat. Astra is in the paid power presets; GPT-6.1 Sol is still rolling out.
• Long-horizon agents: GPT-6.1 Sol adds the multi-agent beta on the Responses API; Astra's edge is ceiling, not features.

Pricing: one-fifth on paper, about 4.5× per task in practice

The identical surcharge structure matters: both models bill prompts over 272K input tokens at 2× input and cache rates and 1.5× output for the whole request, so the one-fifth ratio survives into long-context territory. What the rates mean in practice:

• A straightforward request — 20K uncached input tokens, 4K output: 8¢ on Sol, 40¢ on Astra.
• An agent loop that re-reads a 100K-token context, 90% of it cached, plus 8K of output: roughly 11¢ vs 59¢. Sol's cached rate ($0.10) is 5% of its list input rate; Astra's ($1.00) is 10% of a much higher base. First passes also bill cache writes at 1.25× input on both.
• A 300K-token prompt with 8K of output, over the 272K threshold: $1.32 on Sol, $6.60 on Astra.

Batch and Flex processing halve both models' rates; Fast mode doubles them; Sol's Ultrafast tier is documented at 6× standard. The per-task numbers make the difference concrete: on Artificial Analysis' weighted measure of what a typical benchmark task actually bills, Sol lands at $0.72 against Astra's $3.26 — a 4.5× gap that tracks the list-price ratio.

Benchmarks: 0.9 points on a 52-point scale

The Artificial Analysis Intelligence Index v4.3.2 is a weighted average of ten evaluations — AA-Briefcase, GDPval-AA, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, and AA-LCR. A 0.9-point gap at this level of the field is small enough that task fit matters more than the ranking: run both on your own workload before standardizing.

Where OpenAI does publish an ordering, it keeps Astra on top. The official model-selection ladder runs Luna (Low, then Extra High), then GPT-6.1 Sol (Medium, then Extra High), then Astra (Low, Medium, Extra High) — meaning OpenAI places even Astra's cheapest effort above Sol's most expensive. That ladder is the sharpest statement of where each model belongs: Sol for complex work by default, Astra when quality floors matter more than cost.

Why every benchmark row above comes from one lab: OpenAI's GPT-6.1 Sol announcement is not machine-accessible (the marketing site requires JavaScript), and the API docs publish no per-benchmark Sol-vs-Astra table. Artificial Analysis runs both models through the same harness, which makes it the closest like-for-like record. Its published numbers are for each model's max reasoning effort; the gap at lower efforts can differ — OpenAI's own model-selection ladder places Astra's Low effort above Sol's Extra High.

What's identical between them

• Context window: 1,050,000 tokens, with 922,000 max input and 128,000 max output.
• Knowledge cutoff: April 30, 2026.
• Modalities: text and image input, text output; no audio or video in or out.
• Reasoning efforts: low, medium (default), high, xhigh, max — none and minimal are not supported on either.
• Tool calling via the Responses API, with the same tool set: web search, file search, code interpreter, computer use, hosted shell, apply_patch, skills, MCP, and tool search.
• The over-272K long-prompt surcharge, batch/flex halving, and 2× Fast mode pricing.

The real behavioral difference is OpenAI's positioning: Astra is tuned for the most demanding end-to-end work (and is the model family's alignment showcase, with misalignment monitoring enabled), while Sol exists to do most of that job at a fifth of the bill.

Where you can actually use them

On the API, both are generally available with the same rate-limit tiers. In ChatGPT, both live in Work and Codex — OpenAI states plainly that the GPT-6 models aren't available in consumer Chat. Inside those clients, Astra occupies the upper power presets (Light, Medium, Extra High, with some paid plans omitting Extra High); GPT-6.1 Sol is rolling out separately and appears in Codex as gpt-6.1-sol. Ultrafast-mode access for either model is tied to Pro $500 and eligible Enterprise/Edu plans. If you are on Free or Go, the model you get is GPT-6 Luna — see our Haiku 5.5 vs GPT-6 Luna comparison for that tier.

Motion graphics: both write the animation, neither renders it

Like every current frontier LLM, both models produce motion graphics as code: animated SVG and CSS, GSAP timelines, Lottie JSON, Remotion compositions for video-shaped output, Manim for explainer-style math animation. Neither renders footage — that is a job for a video generation model from our tools index.

Why there is no video-length cost comparison here: these models bill by token, and there is no fixed mapping from tokens to seconds of finished animation — a motion-graphics render is generated as code whose length depends on scene complexity, not on playback duration. Per-second pricing only exists where a model actually outputs footage, which is what the video model price database tracks.

What the verified numbers say about this workload:

• Iteration cost: GPT-6.1 Sol. Regenerating an animation a dozen times bills at one-fifth the token price, and its per-task cost runs about a quarter of Astra's in the same lab.
• Throughput: GPT-6.1 Sol, mildly — 62.4 vs 58.3 tokens per second, with Ultrafast available to both on eligible plans.
• One-shot complex builds: GPT-6 Astra. The official ladder and the 0.9-point index gap both say Astra is the model to trust when a single long composition has to land right the first time.
• Very large project files: a tie — same 1.05M window and identical long-prompt surcharges, so a full Remotion project costs the same relative amount on either.

Worth watching before you standardize:

• Cost in practice — Mark Kashef's build-off rebuilds the same workflow three times across both models and publishes the wall-clock time and dollar cost of each run.
• Agentic benchmarks — RemakeBench's multi-model test puts both OpenAI models through robotics, CAD, and Blender tasks alongside Claude's lineup.
• Codex, five projects — Superbash's head-to-head runs five real builds through both models in Codex.

And when the deliverable is footage rather than vector or UI animation, switch to the video side of this site: the model-vs-model pricing comparisons, the Arena, or free AI video generators.

Which should you pick?

• Pick GPT-6.1 Sol for the default workload: agentic coding, computer use, professional documents, and any pipeline with volume or repeated iterations. OpenAI's own model guidance says to use it for complex coding and agentic workflows when available.
• Pick GPT-6 Astra for the tasks that fail on Sol and cannot fail in production: hardest-step reasoning, science-grade analysis, long autonomous runs. OpenAI's summary: "If cost and latency aren't a concern, you can default to Astra."
• Run both if the workload is mixed: route routine steps through Sol, escalate the failures to Astra, and let the bill — roughly 4.5× apart per task — decide where the line sits.

FAQ

Is GPT-6.1 Sol as good as GPT-6 Astra?

On Artificial Analysis' independent index they are one point apart: 51.8 for GPT-6.1 Sol vs 52.7 for Astra. For the hardest agentic work OpenAI still ranks Astra higher — its model-selection ladder places even Astra's Low effort above Sol's Extra High. For everyday complex work, OpenAI's own guidance is to use GPT-6.1 Sol.

How much cheaper is GPT-6.1 Sol than GPT-6 Astra?

Exactly one-fifth per token: $2 input / $10 output per million tokens, against Astra's $10 / $50. Per finished task the measured gap is similar: $0.72 vs $3.26 on Artificial Analysis' suite, about 22% of Astra's cost.

Do GPT-6.1 Sol and GPT-6 Astra have the same context window?

Yes. Both accept 1,050,000 tokens of context (922,000 max input), output up to 128,000 tokens, share an April 30, 2026 knowledge cutoff, and bill prompts over 272K input tokens at 2× input and 1.5× output rates for the whole request.

Can I use GPT-6.1 Sol or GPT-6 Astra in the ChatGPT app?

Only in ChatGPT Work and Codex — OpenAI states the GPT-6 models are not available in consumer Chat. Astra appears in the paid-tier power presets (Light, Medium, Extra High; some plans omit Extra High), while GPT-6.1 Sol is still rolling out account by account. Free and Go plans run GPT-6 Luna.

Can GPT-6.1 Sol or GPT-6 Astra generate videos or animations?

Not footage. Both generate animation as code: animated SVG and CSS, GSAP timelines, Lottie JSON, Remotion compositions, Manim scenes. Rendered video needs a dedicated video generation model — our tools index and price database cover those.

Which should I use for coding agents?

Start with GPT-6.1 Sol — it is OpenAI's stated recommendation for complex coding and agentic workflows — and promote individual failures to Astra. At one-fifth the token price with a one-point quality gap, Sol absorbs iteration loops far cheaper.

Related comparisons on vidirect

• GPT-6.1 vs GPT-6 — what the 6.1 point release actually changed, and where every GPT-6 model now sits.
• Claude Haiku 5.5 vs GPT-6 Luna — the same price-point battle between Anthropic's small model and OpenAI's.
• MiniMax H3 vs Wan 2.2 — official and reseller per-second pricing for two current video models.
• Kling 2.1 Master vs Veo 3.1 — real output clips from both models playing back to back in the Arena.
• Video model price database — every reseller's per-second price in one filterable table, with per-channel pages like fal's rates.