Gemini 4 Argon vs Claude Opus 5.5
Should you switch from Claude Opus 5.5 to Gemini 4 Argon — and what happens after the Dec 1 price hike?
The short answer
| At a glance | Gemini 4 Argon | Claude Opus 5.5 |
|---|---|---|
| List priceinput / output per 1M tokens | $2 / $10 (better) | $4 / $20 |
| Benchmark task costArtificial Analysis, per Intelligence-Index task | $1.99 (better) | $5.98 |
| Intelligence IndexAA v4.3.2, higher is better | 53 | 58 (better) |
Through November 30, 2026, Gemini 4 Argon ships at half of Claude Opus 5.5's list price ($2/$10 vs $4/$20 per MTok, $0.10 vs $0.20 cached reads) and costs 3x less per Artificial Analysis Intelligence-Index task ($1.99 vs $5.98, emitting 62k vs 119k output tokens). On quality, independent same-harness AA data favors Opus 5.5 overall (Intelligence Index 58 vs 53, SciCode 67% vs 62%, Terminal-Bench 4.0 60% vs 57%), while Argon leads AutomationBench-AA (78% vs 70%) and Google DeepMind's Model Card shows Argon ahead on Vals Index (68.9% vs 67.0%), 256K–1M long-context GraphWalks (84.2% vs 66.8%), and LABBench 2 (88.8% vs 73.1%). Starting December 1, 2026, Argon's introductory pricing expires and list rates match Opus 5.5 ($4/$20) — though Argon's lower reasoning verbosity and free implicit caching still keep task bills lower.
Choose Gemini 4 Argon
SaaS/agent automation (AutomationBench-AA 78% vs 70%), ultra-long context (256K–1M GraphWalks 84.2% vs 66.8%), wet-lab/scientific reasoning, long-decode outputs (>128K up to 1M tokens), or when cutting per-task spend 3x ($1.99 vs $5.98) matters.
Choose Claude Opus 5.5
Hard repository coding and general frontier reasoning where Opus leads same-harness AA evals (Index 58 vs 53, SciCode 67% vs 62%, Terminal-Bench 4.0 60% vs 57%), or when you rely on explicit prompt-cache TTL controls.
Either — decide by evidence you generate
Post-Nov-30 budget planning: once Argon steps up to $4/$20 on 2026-12-01, per-token list prices tie — route by task domain and measured output-token verbosity.
Key facts
- Introductory pricing cliff: Gemini 4 Argon is $2.00 in / $10.00 out / $0.10 cached read per MTok through November 30, 2026 (half of Opus 5.5's $4 / $20 / $0.20). On December 1, 2026, Argon doubles to $4.00 / $20.00 / $0.20 — matching Opus 5.5 on list price.
- Per-task cost (Artificial Analysis v4.3.2 same harness): Argon costs $1.99 vs Opus 5.5's $5.98 per task (3.0x cheaper) at introductory rates, because Argon emits ~half the output tokens per task (62k vs 119k total; 36k vs 84k reasoning).
- Independent same-harness quality (AA v4.3.2): Opus 5.5 leads overall Index (58 vs 53), SciCode (67% vs 62%), Terminal-Bench 4.0 (60% vs 57%), GDPval-AA (1866 vs 1624), and AA-Briefcase (1807 vs 1487); Argon wins AutomationBench-AA (78% vs 70%, #1 overall).
- Vendor Model Card table (Google DeepMind, Oct 2026): Argon leads Opus 5.5 on Vals Index (68.9% vs 67.0%), 256K–1M long-context GraphWalks (84.2% vs 66.8%), and LABBench 2 (88.8% vs 73.1%), while trailing Opus on Terminal-Bench 4.0 (57.4% vs 66.4%).
- Caching model differs fundamentally: Argon supports implicit caching ONLY (95% discount above 4,096 tokens, no explicit cache-write fee, cachedContents API unsupported); Opus 5.5 bills explicit cache writes at $5.00/MTok (5m TTL) or $8.00/MTok (1h TTL).
- Context & output limits: Both offer ~1M context (Argon 1,048,576 unified input+output; Opus 1,000,000) with flat pricing across the window. Argon supports background long decode up to 1M output tokens (65,536 synchronous default) vs Opus's 128,000 max output.
Official pricing, every tier
USD per 1M tokens from vendor pricing pages. 'Not published' means exactly that — we don't extrapolate.
| Service tier | Gemini 4 Argon | Claude Opus 5.5 |
|---|---|---|
| Standarddefault on-demand serving | in $2 · cached $0.10 · out $10 implicit cache only (no write fee) | in $4 · cached $0.20 · out $20 cache write $5 |
| Batchasync jobs, 50% off | in $1 · cached $0.05 · out $5 implicit cache only (no write fee) | in $2 · cached $0.10 · out $10 cache write $2.5 · derived |
| Flexslower/cheaper serving, 50% off | in $1 · cached $0.05 · out $5 implicit cache only (no write fee) | Not published |
| Fastpriority speed, 2x (ex-Priority) | Not published | in $8 · cached $0.40 · out $40 cache write $10 · derived |
| Long-context band | Gemini 4 Argon | Claude Opus 5.5 |
|---|---|---|
| Long-context ratesapplies per request above the model threshold | Flat pricing same rates at any input size (up to context window) | 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. 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) · Google Gemini 4 Argon shows introductory rates valid through 2026-11-30 ($4/$20 standard starting 2026-12-01) and uses implicit caching only (95% discount on cache hits, no explicit write fee). · Google Gemini pricing (verified 2026-10-13)
Tier ≠ quality change
Run this comparison on your own numbers
Preloaded with a workload typical for this comparison. Change anything — results recompute locally.
Token-based workload estimate only; it does not measure quality or cost per successful task.
| Model | Per request | Monthly |
|---|---|---|
| Gemini 4 Argon cheapest | $0.0648 | $1,944.00 |
| Claude Opus 5.5 | $0.1296 | $3,888.00 |
Monthly spread between the cheapest and priciest selected model: $1,944.00.
Breakdown: Gemini 4 Argon · standard tier · standard context
- Fresh input (uncached): 12,000 tok × $2/M$0.024
- Cached input (reads): 8,000 tok × $0.1/M$0.0008
- Cache writes: 0 tok × $0/M$0.00
- Output (incl. reasoning tokens): 4,000 tok × $10/M$0.04
- Per request$0.0648
Rates: official pricing · verified 2026-10-13
- 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 |
|---|---|---|---|---|
| AutomationBenchunspecified⚠ run comparability unknown | ||||
| 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 |
| 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 | ||||
| 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 |
| 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 |
| claude-opus-5-5 | 60 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| gemini-4-argon | 57 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all 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 |
| claude-opus-5-5 | 1866 elo | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| gemini-4-argon | 1624 elo | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all 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 |
| OSWorld2.1 (partial) | ||||
| claude-opus-5-5 | 81.8 % | unspecified | Anthropic announcement evals | Vendor-reported |
| AA Intelligence Indexv4.3.2 | ||||
| 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 all models) | Third-party |
| gemini-4-argon | 53 index-points | config 'Gemini 4 Argon (High)' per AA | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| SciCodeunspecified | ||||
| claude-opus-5-5 | 67 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| gemini-4-argon | 62 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| AA-Briefcasev1.1 | ||||
| claude-opus-5-5 | 1807 elo | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| gemini-4-argon | 1487 elo | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| Humanity's Last Examunspecified | ||||
| claude-opus-5-5 | 61 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| gemini-4-argon | 57 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| AutomationBench-AAunspecified | ||||
| claude-opus-5-5 | 70 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| gemini-4-argon | 78 % | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| AA cost per Intelligence-Index taskv4.3.2 | ||||
| claude-opus-5-5 | 5.98 usd-per-task | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| gemini-4-argon | 1.99 usd-per-task | — | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models) | Third-party |
| Vals Index (DeepMind table)Oct 2026 | ||||
| gemini-4-argon | 68.9 % | — | Google DeepMind Model Card evaluation table (finance, legal, coding, and tax domains) | Vendor-reported |
| claude-opus-5-5 | 67 % | — | Google DeepMind Model Card evaluation table (competitor column) | Vendor-reported |
| gpt-6-astra | 63.1 % | — | Google DeepMind Model Card evaluation table (competitor column) | Vendor-reported |
| GraphWalks (256K–1M long context)BFS F1 | ||||
| gemini-4-argon | 84.2 % | — | Google DeepMind Model Card evaluation table | Vendor-reported |
| claude-opus-5-5 | 66.8 % | — | Google DeepMind Model Card evaluation table (competitor column) | Vendor-reported |
| gpt-6-astra | 71.8 % | — | Google DeepMind Model Card evaluation table (competitor column) | Vendor-reported |
| LABBench 2v2 | ||||
| gemini-4-argon | 88.8 % | — | Google DeepMind Model Card evaluation table | Vendor-reported |
| claude-opus-5-5 | 73.1 % | — | Google DeepMind Model Card evaluation table (competitor column) | Vendor-reported |
| gpt-6-astra | 85.4 % | — | Google DeepMind Model Card evaluation table (competitor column) | Vendor-reported |
| GDP.pdfunspecified | ||||
| claude-opus-5-5 | 26 % | config 'Opus 5.5 (Max, Default Fallback)' per AA | Artificial Analysis Intelligence Index v4.3.2 (independent, same harness) | Third-party |
| AA Time to First Answer Tokenv4.3.2 | ||||
| claude-opus-5-5 | 748.61 seconds | config 'Opus 5.5 (Max, Default Fallback)' per AA | Artificial Analysis Intelligence Index v4.3.2 latency test | Third-party |
| DeepSWEv1.1 | ||||
| claude-opus-5-5 | 74.2 % | max | Meta Muse Wonder announcement evaluation table (competitor column) | Vendor-reported |
| SWE-Marathonunspecified | ||||
| claude-opus-5-5 | 46.3 % | max | Meta Muse Wonder announcement evaluation table (competitor column) | Vendor-reported |
| MemoryBenchunspecified | ||||
| claude-opus-5-5 | 81.5 % | max | Meta Muse Wonder announcement evaluation table (competitor column) | Vendor-reported |
| Harvey LAB-AAunspecified | ||||
| claude-opus-5-5 | 4.2 % | max | Meta Muse Wonder announcement evaluation table (competitor column) | Vendor-reported |
Same benchmark name, multiple rows — run comparability unknown
AutomationBench: 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). Zapier-run. OpenAI's announcement reports a +2.2 pp delta for GPT-6.1 Sol over Opus 5.5 with an unstated underlying runner — the two publications' run relationship is unknown; do not combine.
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: 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)); claude-opus-5-5 = 60 (Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models)); gemini-4-argon = 57 (Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all 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)); claude-opus-5-5 = 1866 (Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all models)); gemini-4-argon = 1624 (Artificial Analysis Intelligence Index v4.3.2 (independent, same harness for all 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: Anthropic — Introducing Claude Opus 5.5 · OpenAI — Introducing GPT-6.1 Sol (DevDay 2026) · Artificial Analysis (independent) — Independent same-harness comparison: GPT-6.1 Sol vs Claude Opus 5.5 (Intelligence Index v4.3.2) · Google DeepMind — Gemini 4 Argon Model Card (October 2026 evaluation table) · Artificial Analysis (independent) — Independent same-harness comparison: Muse Wonder (Max) vs Claude Opus 5.5 (Intelligence Index v4.3.2) · Meta — Put your ideas to work with Muse Wonder and Muse Wonder Full (official release blog & benchmark table)
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 AA SciCode 67% vs 62% and Terminal-Bench 4.0 60% vs 57% favor Opus 5.5, though Argon is 3x cheaper per AA task ($1.99 vs $5.98) through Nov 30. |
| Complex repository maintenance | Claude Opus 5.5 | Measured | Opus leads AA SciCode (67% vs 62%) and Terminal-Bench 4.0 (60% vs 57% AA; 66.4% vs 57.4% in DeepMind's table). |
| Agent workflows | Gemini 4 Argon | Measured | Argon ranks #1 on AA AutomationBench-AA (78% vs 70% same-harness) and leads Vals Index in DeepMind's table (68.9% vs 67.0%) at 1/3 the per-task cost. |
| Long-context tasks | Gemini 4 Argon | Measured | Both models have flat pricing up to 1M tokens, but Argon is half the price through Nov 30 ($2/$10 vs $4/$20), charges no cache-write fee, and leads 256K–1M GraphWalks 84.2% vs 66.8%. |
| Quality-first | Claude Opus 5.5 | Measured | Leads overall AA Intelligence Index (58 vs 53) and 8 of 10 AA evaluations; Argon is the domain pick for scientific/lab reasoning (LABBench 2 88.8% vs 73.1%). |
| Budget-sensitive / high volume | Gemini 4 Argon | Measured | $1.99 vs $5.98 per AA task and $2/$10 vs $4/$20 list price through Nov 30; even after the Dec 1 step-up to $4/$20, lower reasoning token volume (~62k vs ~119k) and zero cache-write fees keep Argon cheaper. |
| Offline batch jobs | Gemini 4 Argon | Price-derived | Explicit Batch and Flex tiers at $1.00/$5.00 through Nov 30 ($2/$10 from Dec 1) vs Opus Batch at $2/$10. |
| Latency-sensitive production | Decide by bake-off | Insufficient evidence | AA has not yet published streaming speed/TTFT for Gemini 4 Argon (Opus 5.5 is 95 tok/s, 666s TTFT). Argon offers low/medium/high thinking levels to trade latency for depth. |
Frequently asked questions
What does Gemini 4 Argon cost compared to Claude Opus 5.5?
Through November 30, 2026, Gemini 4 Argon is priced at $2.00 per 1M input tokens and $10.00 per 1M output tokens ($0.10 cached reads) — exactly half of Claude Opus 5.5's $4.00 / $20.00 ($0.20 cached reads). Starting December 1, 2026, Argon's introductory period ends and its standard rates become $4.00 / $20.00 ($0.20 cached), matching Opus 5.5.
Why is Gemini 4 Argon 3x cheaper per benchmark task if its list price is half?
On Artificial Analysis's Intelligence Index v4.3.2, Argon costs $1.99 per task vs Opus 5.5's $5.98 (3.0x cheaper). In addition to the 2x introductory list-price gap, Argon generates fewer output and reasoning tokens per task (62k total output including 36k reasoning, vs Opus 5.5's 119k total output including 84k reasoning). Even at post-Dec-1 $4/$20 rates, lower token verbosity keeps Argon's per-task spend lower on similar token ratios.
Is Gemini 4 Argon better than Claude Opus 5.5?
It is domain-dependent. On Artificial Analysis's independent same-harness suite (v4.3.2), Opus 5.5 leads the overall Intelligence Index (58 vs 53), SciCode (67% vs 62%), and Terminal-Bench 4.0 (60% vs 57%), while Argon leads AutomationBench-AA (78% vs 70%). In Google DeepMind's Model Card evaluation table, Argon leads on the Vals Index across finance/legal/coding/tax (68.9% vs 67.0%), 256K–1M long-context GraphWalks (84.2% vs 66.8%), and LABBench 2 (88.8% vs 73.1%).
How does context caching differ between Gemini 4 Argon and Claude Opus 5.5?
Gemini 4 Argon supports implicit caching only: prompts of 4,096+ tokens automatically receive a 95% discount ($0.10/MTok through Nov 30; $0.20/MTok from Dec 1) on cache hits with no explicit cache-write fee, and explicit cachedContents creation is not supported. Claude Opus 5.5 uses explicit prompt caching where cache writes cost 1.25x input ($5.00/MTok for 5m TTL, $8.00/MTok for 1h TTL) and reads cost 5% ($0.20/MTok).
Does Gemini 4 Argon have a long-context price cliff?
No. Unlike Gemini 3.1 Pro (which doubled above 200K tokens) or OpenAI's GPT-6 family (which doubles above 272K tokens), Gemini 4 Argon and Claude Opus 5.5 both use flat per-token pricing across their entire ~1M context windows.
Related
Sources & freshness
- Google — Gemini 4 Argon API model documentation (gemini-4-argon specs, 1M unified context, long decode)https://ai.google.dev/gemini-api/docs/models/gemini-4-argon · published 2026-10-13 · accessed 2026-10-13Model code gemini-4-argon: unified 1,048,576 input+output token window, max output 65,536 synchronous default or up to 1,048,576 with background long decode. Thinking levels low/medium/high (default medium). Implicit caching only (95% discount above 4,096 tokens).
- 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.
- 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.
- Google — Gemini Developer API pricing (Gemini 4 Argon standard/batch/flex/priority, context caching)https://ai.google.dev/gemini-api/docs/pricing · accessed 2026-10-13Gemini 4 Argon (gemini-4-argon) paid tier introductory pricing through 2026-11-30: Standard $2.00 input / $10.00 output / $0.10 context caching per 1M tokens ($4.00 / $20.00 / $0.20 starting 2026-12-01). Batch and Flex are 50% off ($1.00 / $5.00 / $0.05 through 2026-11-30; $2.00 / $10.00 / $0.10 starting 2026-12-01). Priority tier is 1.8x ($3.60 / $18.00 / $0.18 through 2026-11-30). Implicit caching only (explicit caching is not supported; no cache-write fee). Not available on the Free tier.
- 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.
- 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.
- 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)').
- Google DeepMind — Gemini 4 Argon Model Card (October 2026 evaluation table)https://deepmind.google/models/model-cards/gemini-4-argon/ · published 2026-10-13 · accessed 2026-10-13Official evaluation table comparing Gemini 4 Argon, GPT-6 Astra, Claude Fable 5.1, and Claude Opus 5.5 across Vals Index (68.9% vs 67.0% Opus), DeepSWE v1.1 (77.9% vs 74.2% Opus), Terminal-Bench 4.0 (57.4% vs 66.4% Opus), GraphWalks 256k-1M (84.2% vs 66.8% Opus), LABBench 2 (88.8% vs 73.1% Opus), and RiemannBench (76.0% vs 69.6% Opus). Knowledge cutoff March 2026.
- Artificial Analysis (independent) — Independent same-harness comparison: Muse Wonder (Max) vs Claude Opus 5.5 (Intelligence Index v4.3.2)https://artificialanalysis.ai/models/comparisons/muse-wonder-vs-claude-opus-5-5 · published 2026-10-12 · accessed 2026-10-13Independent same-harness Intelligence Index v4.3.2 evaluation of Muse Wonder (Max): Index 52 (vs 58 Opus 5.5, 53 Gemini 4 Argon, 52 GPT-6.1 Sol; subscription-only Muse Wonder Full scores 56), cost per Intelligence-Index task $4.52 (54k output tokens/task, 134M output tokens to run Index, $5,733 total) vs $5.98 Opus 5.5 (119k output tokens/task, 260M total), output speed 85 tok/s, time to first answer token 60.82s vs 748.61s Opus 5.5.
- Meta — Put your ideas to work with Muse Wonder and Muse Wonder Full (official release blog & benchmark table)https://dev.meta.ai/resources/blog/build-with-muse-wonder · published 2026-10-12 · accessed 2026-10-13Announces Muse Wonder (muse-wonder-1.0 on Meta Model API and Muse Code) and Muse Wonder Full (exclusively in Muse Code Power/Maximum Usage subscriptions, no pay-as-you-go API). Official evaluation table compares Muse Wonder Full (max), Muse Wonder (max), Claude Fable 5.1 (max), GPT-6 Astra (max), and Claude Opus 5.5 (max) across DeepSWE 1.1 (78.8% Wonder vs 74.2% Opus), SWE-Marathon (53.1% vs 46.3%), MemoryBench (85.6% vs 81.5%), Harvey LAB-AA (10.0% vs 4.2%), Terminal-Bench 4.0-AA (52.0% vs 59.6%), OSWorld V2.1 partial (72.6% vs 81.8%), and GDP.pdf (30.8% vs 30.6%).
Pricing data last verified 2026-10-13. OpenAI ships roughly weekly — re-verify before making spend commitments. See the methodology page for update cadence and stale-data flags.