GPT-6.1 Sol vs Claude Opus 5.5
Which model is better for coding and AI agents?
The short answer
| At a glance | GPT-6.1 Sol | Claude Opus 5.5 |
|---|---|---|
| List priceinput / output per 1M tokens | $2 / $10 (better) | $4 / $20 |
| Cost per taskArtificial Analysis, measured | $0.72 (better) | $5.98 |
| Intelligence IndexAA v4.3.2, higher is better | 52 | 58 (better) |
Independent same-harness evidence now exists (Artificial Analysis, Intelligence Index v4.3.2, both models through one harness), and it splits the decision cleanly. Quality: Opus 5.5 leads on 8 of 10 published evals — Intelligence Index 58 vs 52, SciCode 67% vs 54%, GDPval-AA 1866 vs 1592, Terminal-Bench 4.0 60% vs 56%. Cost per task: GPT-6.1 Sol wins decisively — $0.72 vs $5.98 per AA-measured task (8.3x), because Opus emits ~3x the output tokens per task (119k vs 38k, mostly reasoning). Latency is split: Opus generates faster (97 vs 59 tok/s) but Sol answers sooner (first token 309s vs 704s). So: choose Opus when the last points of quality on hard professional/coding work are worth ~8x the task cost; choose Sol when task economics dominate — and note Sol even wins GDP.pdf (31% vs 26%). List pricing ($2/$10 vs $4/$20) and the >272K cliff still favor Sol at every input size.
Choose GPT-6.1 Sol
Cost-per-task matters: $0.72 vs $5.98 per AA-measured task, half the list price at every input size, faster time-to-first-token (309s vs 704s), and it even wins GDP.pdf (31% vs 26%).
Choose Claude Opus 5.5
Maximum quality on hard work is worth the token bill: leads 8/10 same-harness evals (SciCode 67% vs 54%, Index 58 vs 52) and generates ~1.6x faster once started (97 vs 59 tok/s).
Either — decide by evidence you generate
Mixed fleets: route the hard 10% to Opus and the volume to Sol — the calculator prices your split; the quality gaps above are same-harness measured, not vendor claims.
Key facts
- Standard list price: 6.1 Sol $2 / $10 vs Opus 5.5 $4 / $20 per MTok — 2x gap both directions.
- INDEPENDENT same-harness (Artificial Analysis Intelligence Index v4.3.2): Opus 5.5 leads 8 of 10 evals — Index 58 vs 52, SciCode 67% vs 54%, GDPval-AA 1866 vs 1592, AA-Briefcase 1807 vs 1557, Terminal-Bench 4.0 60% vs 56%, HLE 61% vs 53%, AutomationBench-AA 70% vs 65%; Sol wins GDP.pdf 31% vs 26%; CritPt tied 32/32.
- Per-task cost (AA-measured): $0.72 vs $5.98 per Intelligence-Index task — an 8.3x gap at only 2x list prices, because Opus emits ~3x the output tokens per task (119k vs 38k; 84k vs 25k reasoning). Per-token price ratios understate per-task gaps.
- Latency split (AA): Opus generates faster (97 vs 59 tok/s output); Sol reaches first token far sooner (309s vs 704s median on Index tasks).
- Long context: OpenAI doubles above 272K input tokens per request (6.1 Sol → $4/$15); Anthropic publishes NO surcharge for Opus 5.5 (flat $4/$20). Even in the long band, 6.1 Sol input matches Opus and output is 25% cheaper ($15 vs $20).
- Cache: 6.1 Sol reads $0.10, writes $2.50; Opus reads $0.20, writes $5.00 (5m TTL; 1h $8 published, not modeled). Batch −50% both, stackable.
- Vendor tables remain separately stored (Zapier-run AutomationBench; OpenAI-cited figures) and are never merged with AA's runs — every row carries publisher + runner + harness.
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 | Claude Opus 5.5 |
|---|---|---|
| Standarddefault on-demand serving | in $2 · cached $0.10 · out $10 cache write $2.5 · >272K in/req | in $4 · cached $0.20 · out $20 cache write $5 |
| Batchasync jobs, 50% off | in $1 · cached $0.05 · out $5 cache write $1.25 · >272K in/req | in $2 · cached $0.10 · out $10 cache write $2.5 · derived |
| Flexslower/cheaper serving, 50% off | in $1 · cached $0.05 · out $5 cache write $1.25 · >272K in/req | Not published |
| Fastpriority speed, 2x (ex-Priority) | in $4 · cached $0.20 · out $20 cache write $5 · >272K in/req | in $8 · cached $0.40 · out $40 cache write $10 · derived |
| Ultrafast6x standard price | in $12 · cached $0.60 · out $60 cache write $15 · >272K in/req | Not published |
| Long-context band | GPT-6.1 Sol | Claude Opus 5.5 |
|---|---|---|
| Long-context ratesapplies per request above the model threshold | in $4 · cached $0.20 · out $15 cache write $5 · >272K in/req | Flat pricing same rates at any input size (up to context window) |
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) · Anthropic rows bill the 5-minute cache-write TTL; the 1-hour TTL ($8/MTok standard) is also published, and cache multipliers stack with the Batch discount and Fast-mode pricing. · Anthropic 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 | $0.0462 | $1,386.00 |
| Claude Opus 5.5 | $0.0924 | $2,772.00 |
Monthly spread between the cheapest and priciest selected model: $1,386.00.
Breakdown: GPT-6.1 Sol · standard tier · standard context
- Fresh input (uncached): 8,000 tok × $2/M$0.016
- Cached input (reads): 2,000 tok × $0.1/M$0.0002
- Cache writes: 0 tok × $2.5/M$0.00
- Output (incl. reasoning tokens): 3,000 tok × $10/M$0.03
- Per request$0.0462
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 |
| claude-opus-5-5 | 40 % | unspecified | Zapier (Opus 5.5 measured during Zapier's early access), published by Anthropic | Third-party |
| gpt-6-astra | 41.4 % | unspecified | Zapier public leaderboard, cited by Anthropic | Third-party |
| OSWorld2.0 offline set, v2026.08.08⚠ run comparability unknown | ||||
| gpt-6.1-sol | within 2.1 pp of GPT-6 Astra; +7 pp vs GPT-6 Sol | max | OpenAI announcement evals | Vendor-reported |
| claude-opus-5-5 | 81.8 % | unspecified | Anthropic 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 |
| claude-opus-5-5 | 58.7 % | max (adaptive thinking) | Anthropic announcement evals | 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 |
| claude-opus-5-5 | 23.21 usd-per-task | unspecified | OpenAI announcement cost table (who measured the competitor's cost is not stated) | 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 | ||||
| claude-opus-5-5 | 66.4 % | xhigh (adaptive thinking) | Anthropic announcement evals | Vendor-reported |
| 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 |
| claude-opus-5-5 | 60 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models) | Third-party |
| FrontierCodev1.1 (Main)⚠ run comparability unknown | ||||
| claude-opus-5-5 | 54.4 % | max (adaptive thinking) | Anthropic announcement evals | Vendor-reported |
| claude-opus-5-5 | 54.6 % | medium (default) | Anthropic announcement evals | Vendor-reported |
| gpt-6-astra | 53.3 % | unspecified | Anthropic announcement evals (competitor row; no runner footnote — assumed Anthropic-run) | Vendor-reported |
| CursorBench4.0 | ||||
| claude-opus-5-5 | 57.8 % | max (adaptive thinking) | Anthropic announcement evals | Vendor-reported |
| GDPval-AAv2.1⚠ run comparability unknown | ||||
| claude-opus-5-5 | 1846 elo | unspecified | Anthropic announcement evals | Vendor-reported |
| 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 |
| claude-opus-5-5 | 1866 elo | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for both models) | Third-party |
| Humanity's Last Exam (with tools)unspecified⚠ run comparability unknown | ||||
| claude-opus-5-5 | 67.7 % | unspecified | Anthropic announcement evals | Vendor-reported |
| 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 |
| claude-opus-5-5 | 58 index-points | config 'Opus 5.5 (Max, Default Fallback)' 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 |
| claude-opus-5-5 | 67 % | — | 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 |
| claude-opus-5-5 | 1807 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 |
| claude-opus-5-5 | 61 % | — | 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 |
| claude-opus-5-5 | 70 % | — | 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 |
| claude-opus-5-5 | 5.98 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)); claude-opus-5-5 = 40 (Zapier (Opus 5.5 measured during Zapier's early access), published by Anthropic); 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.
OSWorld: gpt-6.1-sol = within 2.1 pp of GPT-6 Astra; +7 pp vs GPT-6 Sol (OpenAI announcement evals); claude-opus-5-5 = 81.8 (Anthropic announcement evals). Whether these rows share a run is not documented in the sources; treat them as not comparable.
Terminal-Bench-Science: gpt-6-astra = 68.1 (OpenAI announcement evals); gpt-6-astra = 64.6 (OpenAI-reported figure, cited by Anthropic (footnote 3)); claude-opus-5-5 = 58.7 (Anthropic announcement evals). 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); claude-opus-5-5 = 23.21 (OpenAI announcement cost table (who measured the competitor's cost is not stated)); 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: claude-opus-5-5 = 66.4 (Anthropic announcement evals); 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)); claude-opus-5-5 = 60 (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.
FrontierCode: claude-opus-5-5 = 54.4 (Anthropic announcement evals); claude-opus-5-5 = 54.6 (Anthropic announcement evals); gpt-6-astra = 53.3 (Anthropic announcement evals (competitor row; no runner footnote — assumed Anthropic-run)). Whether these rows share a run is not documented in the sources; treat them as not comparable.
GDPval-AA: claude-opus-5-5 = 1846 (Anthropic announcement evals); 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)); claude-opus-5-5 = 1866 (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.
Humanity's Last Exam (with tools): claude-opus-5-5 = 67.7 (Anthropic announcement evals); gpt-6-astra = 57.2 (Anthropic announcement evals (competitor row; no runner footnote — assumed Anthropic-run)). 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 | Claude Opus 5.5 | Measured | Same-harness SciCode 67% vs 54% and Terminal-Bench 4.0 60% vs 56%; if you'd rather trade those points for 8x cheaper tasks, Sol is defensible — the split is real, not vendor spin. |
| Complex repository maintenance | Claude Opus 5.5 | Measured | SciCode (67 vs 54) is repo-scale coding; Anthropic's long-horizon case studies point the same way. AA effort configs as labeled ('Sol (Max)' vs 'Opus 5.5 (Max, Default Fallback)'). |
| Agent workflows | Claude Opus 5.5 | Measured | AA AutomationBench-AA 70% vs 65% and GDPval-AA 1866 vs 1592 in the same run — the earlier 'conflicting vendor claims' picture is settled by independent data (narrowly). |
| Long-context tasks | GPT-6.1 Sol | Price-derived | Pure pricing: input ties at $4 above 272K, Sol output 25% cheaper ($15 vs $20). Pricing arithmetic only. |
| Quality-first | Claude Opus 5.5 | Measured | 8/10 same-harness evals including the biggest coding gaps. |
| Budget-sensitive / high volume | GPT-6.1 Sol | Measured | $0.72 vs $5.98 per AA-measured task; half the list price; cheaper at every input size. |
| Offline batch jobs | GPT-6.1 Sol | Measured | Batch $1/$5 vs $2/$10 — same discount, lower base; token verbosity widens the gap further. |
| Latency-sensitive production | Decide by bake-off | Measured | Genuinely split: Opus 97 vs 59 tok/s throughput; Sol 309s vs 704s to first token. Pick by which latency your users feel. |
The 2-week bake-off protocol
Public benchmarks don't run your tasks. Before committing a fleet, measure both models on your own work.
- 1Pick 30–50 real tasks spanning your actual mix (not demo prompts): coding, agents, docs, failure cases.
- 2Run both models at your target effort levels; capture outputs, latency, token counts (in/out/cached), retries, and fallbacks.
- 3Grade blind if possible: shuffle outputs, grade with rubric or a third model you trust, record win/tie/loss per task.
- 4Compute $/task from YOUR measured token counts with our calculator — not from list prices.
- 5Decide per workload, not globally: it's normal to land on 6.1 Sol for chat/autocomplete and Opus 5.5 for long-horizon refactors.
Frequently asked questions
Which is better, GPT-6.1 Sol or Claude Opus 5.5?
Independent same-harness data (Artificial Analysis Intelligence Index v4.3.2) answers this now: Opus 5.5 is the stronger model on 8 of 10 evals (Index 58 vs 52; SciCode 67% vs 54%), but Sol wins the economics — $0.72 vs $5.98 per measured task — and even takes GDP.pdf (31% vs 26%). Better depends on whether you're buying quality points or tasks.
Which model is cheaper?
Per token, 6.1 Sol is half the list price ($2/$10 vs $4/$20). Per TASK the gap is bigger than 2x: AA measured $0.72 vs $5.98, because Opus writes ~3x more tokens per task. Above 272K inputs Sol doubles to $4/$15 while Opus stays flat — Sol still cheaper on output.
Can I trust these numbers?
The AA figures are independent and same-harness for both models — the strongest evidence class we cite. Vendor-run tables (Anthropic's, OpenAI's, Zapier's) are kept separately with publisher+runner+harness on every row and never merged with AA's runs.
What about latency?
Split, per AA: Opus generates ~1.6x faster (97 vs 59 tokens/s) but Sol reaches first token ~2.3x sooner (309s vs 704s median on Index tasks, thinking included). If users wait for complete answers, Opus; if streaming first bytes, Sol.
Does Opus 5.5 charge extra above 200K tokens?
No long-context surcharge is published for Opus 5.5 (1M window, flat $4/$20). GPT-6 family models double above 272K input tokens per request — for this pair even the doubled band keeps Sol cheaper on output.
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).
- Anthropic — Claude models overview (model IDs, context window, max output, retirement)https://platform.claude.com/docs/en/docs/about-claude/models/overview · accessed 2026-10-09claude-opus-5-5: 1M context, 128K max output, training cutoff Jun 2026, retirement not sooner than 2027-09-22, cache reads '5% of input price', batch 50% off.
- 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.
- Anthropic — Claude pricing (API pricing table incl. cache write/read, fast mode, batch)https://claude.com/pricing · accessed 2026-10-09Opus 5.5: $4/$20 per MTok, cache read $0.20, cache write (5m TTL) $5, batch 'save 50%', fast mode 2x on input/output.
- Anthropic — Claude pricing docs (cache TTL prices, fast/batch stacking rules)https://platform.claude.com/docs/en/about-claude/pricing · accessed 2026-10-09Opus 5.5 1h cache write $8/MTok; caching multipliers apply on top of fast-mode pricing and stack with the Batch discount; fast mode not available with Batch.
- 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.