GPT-6.1 Sol vs GPT-6 Astra
Which model should I use, and is Astra worth the higher cost?
The short answer
| At a glance | GPT-6.1 Sol | GPT-6 Astra |
|---|---|---|
| List priceinput / output per 1M tokens | $2 / $10 (better) | $10 / $50 |
For most workloads, GPT-6.1 Sol is the rational default: it matches or comes within a couple of points of Astra across OpenAI's own published evals (DeepSWE, OSWorld, factuality) at one-fifth of the standard-tier price. Astra still earns its premium on the hardest terminal-and-science-style tasks — it leads Terminal-Bench-Science at 68.1% in OpenAI's run — and it is the ceiling pick when a 2–3 point quality gap is worth 5x the token price. Run the numbers for your own workload before deciding: per-token price gaps and per-task cost gaps are not the same thing.
Choose GPT-6.1 Sol
Default choice: coding, agents, document work, anything cost-sensitive. Near-Astra quality at ~1/5 standard price; cached input at $0.10/MTok is 10x cheaper than Astra's $1.00 (note: GPT-6 Luna caches at $0.01 — the cheapest in the GPT-6 family).
Choose GPT-6 Astra
Hardest tasks where its measured lead is real (Terminal-Bench-Science 68.1% vs 6.1 Sol's 'more than double GPT-6 Sol'), maximum-quality mandates, or when Ultrafast at Astra-tier capability is required.
Key facts
- Standard tier: 6.1 Sol $2 / $10 (in/out per MTok) vs Astra $10 / $50 — a 5x gap (OpenAI pricing, verified 2026-10-09).
- OpenAI's own announcement: 6.1 Sol 'matches GPT-6 Astra' on DeepSWE v1.1 (75.2% at high effort) and stays within 1.9 pp on factuality at <1/5 the cost.
- Where Astra leads: Terminal-Bench-Science 0.1 at 68.1% (OpenAI harness); 6.1 Sol more than doubles GPT-6 Sol there but trails Astra.
- Per-task cost on Terminal-Bench-Science (OpenAI's run): 6.1 Sol $5.47/task vs Astra $23.80/task vs Opus 5.5 $23.21/task.
- Tier multipliers (both models): Batch/Flex = 50% of Standard; Fast = 2x; Ultrafast = 6x. Long-context (>272K input tokens per request) roughly doubles rates.
- Cache writes are billed per token at the write rate — 6.1 Sol $2.50/MTok (short context, standard tier; $1.25 on batch/flex, $5 on fast, $15 on ultrafast), Astra 5x higher at $12.50 — and a write REPLACES the input rate for those tokens rather than adding to it (official bucket rule). Cache-heavy agent loops should model writes, not just reads.
Official pricing, every tier
USD per 1M tokens from vendor pricing pages. 'Not published' means exactly that — we don't extrapolate.
| Service tier | GPT-6.1 Sol | GPT-6 Astra |
|---|---|---|
| Standarddefault on-demand serving | in $2 · cached $0.10 · out $10 cache write $2.5 · >272K in/req | in $10 · cached $1 · out $50 cache write $12.5 · >272K in/req |
| Batchasync jobs, 50% off | in $1 · cached $0.05 · out $5 cache write $1.25 · >272K in/req | in $5 · cached $0.50 · out $25 cache write $6.25 · >272K in/req |
| Flexslower/cheaper serving, 50% off | in $1 · cached $0.05 · out $5 cache write $1.25 · >272K in/req | in $5 · cached $0.50 · out $25 cache write $6.25 · >272K in/req |
| Fastpriority speed, 2x (ex-Priority) | in $4 · cached $0.20 · out $20 cache write $5 · >272K in/req | in $20 · cached $2 · out $100 cache write $25 · >272K in/req |
| Ultrafast6x standard price | in $12 · cached $0.60 · out $60 cache write $15 · >272K in/req | in $60 · cached $6 · out $300 cache write $75 · >272K in/req |
| Long-context band | GPT-6.1 Sol | GPT-6 Astra |
|---|---|---|
| Long-context ratesapplies per request above the model threshold | in $4 · cached $0.20 · out $15 cache write $5 · >272K in/req | in $20 · cached $2 · out $75 cache write $25 · >272K in/req |
USD per 1M tokens. Every input token is billed in exactly one bucket — uncached, cached read, or cache write; a write replaces the input rate for those tokens (official rule), so write rates are per tier as listed.OpenAI long context = input > 272,000 tokens per single request. · OpenAI pricing (verified 2026-10-09)
Tier ≠ quality change
Run this comparison on your own numbers
Preloaded with a workload typical for this comparison. Change anything — results recompute locally.
| Model | Per request | Monthly |
|---|---|---|
| GPT-6.1 Sol cheapest | $0.0414 | $1,656.00 |
| GPT-6 Astra | $0.209 | $8,360.00 |
Monthly spread between the cheapest and priciest selected model: $6,704.00.
Breakdown: GPT-6.1 Sol · standard tier · standard context
- Fresh input (uncached): 8,000 tok × $2/M$0.016
- Cached input (reads): 4,000 tok × $0.1/M$0.0004
- Cache writes: 0 tok × $2.5/M$0.00
- Output (incl. reasoning tokens): 2,500 tok × $10/M$0.025
- Per request$0.0414
Rates: official pricing · verified 2026-10-09
- Per-token arithmetic only: it does not model quality differences or cross-vendor output-token-count differences for the same task.
- Reasoning tokens are billed as output tokens at the chosen model's output rate.
- Long-context band is decided per request by total input tokens vs the provider threshold, not by monthly volume.
- Cache accounting uses the official mutually-exclusive buckets: each input token is billed once, as uncached input, cached read, OR cache write — writes replace the input rate for those tokens, never add to it.
- Claude rows bill the 5-minute-TTL cache write; the published 1-hour TTL ($8/MTok standard) is not modeled (no TTL input).
- Rows flagged 'derived' are computed from a stated official rule (e.g. Anthropic batch -50%), not read verbatim from a price table.
- Missing official prices render as Unsupported — we never apply a guessed multiplier (e.g., GPT-6 Sol has no Ultrafast tier).
What the evidence actually says
Independent and vendor-run evals, each with publisher, runner, and harness. Numbers from different runs are never merged.
| Benchmark | Score | Effort | Harness | Evidence |
|---|---|---|---|---|
| DeepSWEv1.1 | ||||
| gpt-6.1-sol | 75.2 % | high | OpenAI announcement evals | Vendor-reported |
| AutomationBench1.0.6⚠ run comparability unknown | ||||
| gpt-6.1-sol | +2.2 pp vs Claude Opus 5.5; +4.8 pp vs GPT-6 Sol | medium | OpenAI announcement (underlying runner not stated — AutomationBench is run/reported by Zapier per Anthropic's footnote) | Vendor-reported |
| gpt-6-astra | 41.4 % | unspecified | Zapier public leaderboard, cited by Anthropic | Third-party |
| OSWorld2.0 offline set, v2026.08.08 | ||||
| gpt-6.1-sol | within 2.1 pp of GPT-6 Astra; +7 pp vs GPT-6 Sol | max | OpenAI announcement evals | Vendor-reported |
| Terminal-Bench-Science0.1⚠ run comparability unknown | ||||
| gpt-6-astra | 68.1 % | unspecified | OpenAI announcement evals | Vendor-reported |
| gpt-6-astra | 64.6 % | unspecified | OpenAI-reported figure, cited by Anthropic (footnote 3) | Vendor-reported |
| Terminal-Bench-Science cost per task0.1⚠ run comparability unknown | ||||
| gpt-6.1-sol | 5.47 usd-per-task | unspecified | OpenAI announcement evals | Vendor-reported |
| gpt-6-astra | 23.8 usd-per-task | unspecified | OpenAI announcement evals | Vendor-reported |
| Factuality error rateunspecified | ||||
| gpt-6.1-sol | 7.7 error-rate-% | low | OpenAI announcement evals | Vendor-reported |
| GDP.pdf (professional PDF Q&A)unspecified | ||||
| gpt-6.1-sol | scores higher than Opus 5.5 with fallbacks at less than half the cost per task; near Astra at ~1/5 cost per task | multiple settings | OpenAI announcement evals | Vendor-reported |
| Terminal-Bench4.0⚠ run comparability unknown | ||||
| gpt-6-astra | 57.9 % | high (as reported by OpenAI) | OpenAI-reported figure, cited by Anthropic (footnote 1) | Vendor-reported |
| gpt-6.1-sol | 56 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models) | Third-party |
| FrontierCodev1.1 (Main) | ||||
| gpt-6-astra | 53.3 % | unspecified | Anthropic announcement evals (competitor row; no runner footnote — assumed Anthropic-run) | Vendor-reported |
| GDPval-AAv2.1⚠ run comparability unknown | ||||
| gpt-6-astra | 1542 elo | unspecified | Anthropic announcement evals (competitor row; no runner footnote — assumed Anthropic-run) | Vendor-reported |
| gpt-6.1-sol | 1592 elo | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models) | Third-party |
| Humanity's Last Exam (with tools)unspecified | ||||
| gpt-6-astra | 57.2 % | unspecified | Anthropic announcement evals (competitor row; no runner footnote — assumed Anthropic-run) | Vendor-reported |
| AA Intelligence Indexv4.3.2 | ||||
| gpt-6.1-sol | 52 index-points | config 'Sol (Max)' per AA | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models) | Third-party |
| SciCodeunspecified | ||||
| gpt-6.1-sol | 54 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models) | Third-party |
| AA-Briefcasev1.1 | ||||
| gpt-6.1-sol | 1557 elo | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models) | Third-party |
| Humanity's Last Examunspecified | ||||
| gpt-6.1-sol | 53 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models) | Third-party |
| AutomationBench-AAunspecified | ||||
| gpt-6.1-sol | 65 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models) | Third-party |
| AA cost per Intelligence-Index taskv4.3.2 | ||||
| gpt-6.1-sol | 0.72 usd-per-task | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models) | Third-party |
Same benchmark name, multiple rows — run comparability unknown
AutomationBench: gpt-6.1-sol = +2.2 pp vs Claude Opus 5.5; +4.8 pp vs GPT-6 Sol (OpenAI announcement (underlying runner not stated — AutomationBench is run/reported by Zapier per Anthropic's footnote)); gpt-6-astra = 41.4 (Zapier public leaderboard, cited by Anthropic). Do NOT compute 6.1 Sol = 40.0 + 2.2: the 40.0 is Zapier-run, the +2.2 is OpenAI-published; run relationship unknown.
Terminal-Bench-Science: gpt-6-astra = 68.1 (OpenAI announcement evals); gpt-6-astra = 64.6 (OpenAI-reported figure, cited by Anthropic (footnote 3)). Anthropic's announcement also carries an Astra TBS figure of 64.6%, footnoted there as 'as reported by OpenAI' — BOTH figures are OpenAI-published, yet they differ (68.1 vs 64.6). Which runs/versions/dates produced each figure is not documented in either announcement; we store both and merge nothing.
Terminal-Bench-Science cost per task: gpt-6.1-sol = 5.47 (OpenAI announcement evals); gpt-6-astra = 23.8 (OpenAI announcement evals). Whether these rows share a run is not documented in the sources; treat them as not comparable.
Terminal-Bench: gpt-6-astra = 57.9 (OpenAI-reported figure, cited by Anthropic (footnote 1)); gpt-6.1-sol = 56 (Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models)). Whether these rows share a run is not documented in the sources; treat them as not comparable.
GDPval-AA: gpt-6-astra = 1542 (Anthropic announcement evals (competitor row; no runner footnote — assumed Anthropic-run)); gpt-6.1-sol = 1592 (Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models)). Whether these rows share a run is not documented in the sources; treat them as not comparable.
We never average or merge these. See methodology.Sources: OpenAI — Introducing GPT-6.1 Sol (DevDay 2026) · Anthropic — Introducing Claude Opus 5.5 · Artificial Analysis (independent) — Independent same-harness comparison: GPT-6.1 Sol vs Claude Opus 5.5 (Intelligence Index v4.3.2)
Which model for which workload
Confidence labels: Measured = same-harness evidence · Inferred = reasoned from documented evidence · Insufficient evidence = no comparable public data.
| Workload | Our pick | Confidence | Why |
|---|---|---|---|
| Daily coding tasks | GPT-6.1 Sol | Measured | DeepSWE v1.1 parity with Astra (75.2% vs 'matches Astra') at 1/5 the price; factuality within 1.9 pp. Evidence is OpenAI's own harness; same-vendor comparison only. |
| Complex repository maintenance | GPT-6.1 Sol | Inferred | Near-Astra agentic-coding results at a fraction of cost; no published Astra-exclusive lead on repo-scale coding in the fetched announcements. |
| Agent workflows | GPT-6.1 Sol | Measured | OSWorld 2.0: within 2.1 pp of Astra at ~1/7 the per-task cost (OpenAI statement); AutomationBench +4.8 pp over GPT-6 Sol. If your agents hit terminal/science-style tool chains, check the Terminal-Bench-Science gap below. |
| Long-context tasks | GPT-6.1 Sol | Price-derived | Both models double above 272K input tokens per request; 6.1 Sol's long-context rates ($4/$15) remain far below Astra's ($20/$75). |
| Quality-first | GPT-6 Astra | Measured | Only category with a clear measured Astra lead in OpenAI's own table: Terminal-Bench-Science 68.1%. |
| Budget-sensitive / high volume | GPT-6.1 Sol | Price-derived | 5x cheaper at standard tier; identical batch/flex halving; cached-input reads at $0.10 vs Astra's $1.00 (GPT-6 Luna is cheaper still at $0.01, but is not a like-for-like replacement for Astra-class work). If quality floor allows, GPT-6 Luna at $0.10/$0.50 may beat both — separate comparison. |
| Offline batch jobs | GPT-6.1 Sol | Price-derived | Batch/Flex at $1/$5 per MTok (short context); quality retention documented on DeepSWE/factuality. |
| Latency-sensitive production | GPT-6.1 Sol | Price-derived | Fast tier at $4/$20 (2x) or Ultrafast at $12/$60 (6x) keeps latency spend bounded; Astra Ultrafast at $60/$300 is the ceiling option when both speed AND max capability are non-negotiable. |
Frequently asked questions
Is GPT-6.1 Sol really as capable as GPT-6 Astra?
On most of OpenAI's published evals the gap is 0–2 points: DeepSWE v1.1 is described as a match (75.2% at high effort), factuality is within 1.9 pp, OSWorld within 2.1 pp. The clear exception is Terminal-Bench-Science 0.1, where Astra leads at 68.1%. All of these are OpenAI-run numbers — strong same-harness evidence, but still vendor-reported.
When is Astra actually worth 5x the price?
Three documented cases: tasks shaped like Terminal-Bench-Science (hard terminal/science tool chains) where Astra's lead is measured; maximum-quality mandates where a 2–3 point expected gap justifies the premium; and Ultrafast-tier Astra when top capability AND 6x-speed serving are both required. Otherwise the per-task savings of 6.1 Sol (e.g. $5.47 vs $23.80 on TBS) dominate.
Does the 5x price gap mean a 5x total-cost gap?
Not necessarily, and sometimes it's worse than 5x: if a workload forces Astra's long-context band (>272K input tokens per request), rates go to $20/$75 while 6.1 Sol goes to $4/$15 — a 5x input gap but a 5x output gap too. Conversely, if models emit different output token counts for the same task, per-task cost can diverge from per-token ratios. Use the calculator with your real token counts.
What happens to pricing above 272K input tokens?
Both models switch to long-context rates: 6.1 Sol $4 in / $15 out, Astra $20 in / $75 out per MTok. The threshold is judged per request, not per month. Cached-input and cache-write rates also step up (see the pricing table).
Is Astra's cached input cheaper than 6.1 Sol's standard input?
No. Astra cached reads cost $1.00/MTok — still 10x 6.1 Sol's cached rate ($0.10) and half of 6.1 Sol's uncached rate ($2.00). Caching narrows gaps within a model far more than between models.
Related
Sources & freshness
- OpenAI — OpenAI API model catalog (model IDs, context windows, reasoning efforts, capabilities)https://developers.openai.com/api/docs/models · accessed 2026-10-09GPT-6 Sol is NOT listed in the catalog front page at access time (still priced on the pricing page). Knowledge cutoffs: Apr 30 2026 (Astra/6.1 Sol), May 18 2026 (Luna).
- OpenAI — OpenAI API pricing (GPT-6 family tier matrix: standard/batch/flex/fast/ultrafast, short/long context, cache)https://developers.openai.com/api/docs/pricing · accessed 2026-10-09Long context defined as >272K input tokens per request (2x input and cache rates, 1.5x output for the whole request). 'Priority processing' renamed 'Fast mode' on 2026-07-30. Flex and Batch are half of Standard for the GPT-6 family, Fast is 2x, Ultrafast is 6x. Cache writes cost 1.25x the uncached input rate, reads 0.1x (0.05x on GPT-6.1 Sol). Cache-write rates are captured on every published tier row, not Standard only.
- OpenAI — Introducing GPT-6.1 Sol (DevDay 2026)https://openai.com/index/introducing-gpt-6-1-sol · published 2026-09-29 · accessed 2026-10-09Self-reported evals (OpenAI harness). Includes per-task cost quotes (Terminal-Bench-Science). Announces GPT-6.1 Sol Ultrafast 'coming soon' in Codex. Availability: ChatGPT Work + Codex, not yet in Chat.
- Anthropic — Introducing Claude Opus 5.5https://www.anthropic.com/claude-opus-5-5 · published 2026-09-22 · accessed 2026-10-09Self-reported evals (Anthropic harness, adaptive thinking, max effort unless noted). Includes competitor scores incl. GPT-6 Astra as measured by Anthropic.
- Artificial Analysis (independent) — Independent same-harness comparison: GPT-6.1 Sol vs Claude Opus 5.5 (Intelligence Index v4.3.2)https://artificialanalysis.ai/models/comparisons/gpt-6-1-sol-vs-claude-opus-5-5 · accessed 2026-10-10Independent same-harness evaluation of both models. Cost-per-task = weighted average per Intelligence Index task; reflects measured token verbosity. Effort configs as labeled by AA ('Sol (Max)' vs 'Opus 5.5 (Max, Default Fallback)').
Pricing data last verified 2026-10-09. OpenAI ships roughly weekly — re-verify before making spend commitments. See the methodology page for update cadence and stale-data flags.