Checked November 12, 2026. This is a comparison of documented model choices and workflow considerations—not a benchmark we ran or a claim that every ChatGPT or Claude conversation uses the same model.
“ChatGPT or Claude?” mixes several decisions: which model, which app, which tools, which account plan, and which data policy. A fair comparison starts by naming the exact model and the environment rather than assigning a single score to a brand.
OpenAI's current catalog distinguishes GPT-6 Astra, GPT-6.1 Sol, GPT-6.1 Terra, and GPT-6 Luna by intended workload. Anthropic's current lineup includes Fable 5.2, Opus 5.5, Sonnet 5.5, and Haiku 5.5.[13][7]
Original editorial framework. No scores or cross-provider benchmark results are implied.
1. The model choices are not a single ladder
GPT-6 Astra: OpenAI positions it for its most demanding reasoning and coding work. It is the high-priced general-purpose option in this snapshot, not a reason to run every classification job on the same model.[15]
GPT-6.1 Sol: positioned as a balance of capability and cost for complex coding and professional agent workflows. Its model page also distinguishes the default 500K-token context in Codex and ChatGPT Work from the larger API capacity.[14]
GPT-6.1 Terra: emphasizes visual reasoning, computer use, and writing. That makes it a candidate to evaluate for diagram-heavy or interface-driven work, not proof it will win every task against Sol or Astra.[16]
GPT-6 Luna: targets focused, high-volume work. Our recommendation is to include it in the baseline when the task is narrow and easy to verify, rather than assuming a frontier-priced model is necessary.[17]
Claude Fable 5.2: released November 9, is positioned for demanding reasoning and long-horizon work. Anthropic recommends starting with Opus for most workloads and escalating when evaluation results still fall short.[1][3]
Claude Opus 5.5, Sonnet 5.5, and Haiku 5.5: their model pages respectively emphasize long-running coding/knowledge work, a balance of speed and intelligence, and high-volume latency-sensitive tasks.[20][21][22]
These are provider descriptions. They help select candidates for a test; they are not independent head-to-head findings.
2. API price snapshot: specify the conditions
The table shows standard synchronous USD prices per million tokens, before caching, batch discounts, premium speed modes, regional adjustments, tool charges, or taxes. It is not app-subscription pricing.[19][7]
| Model | API model ID | Input / output per 1M tokens | Published API context |
|---|---|---|---|
| GPT-6 Astra | gpt-6-astra |
$10 / $50* | 1,050,000[15] |
| GPT-6.1 Sol | gpt-6.1-sol |
$2 / $10* | 1,050,000[14] |
| GPT-6.1 Terra | gpt-6.1-terra |
$1 / $5* | 1,050,000[16] |
| GPT-6 Luna | gpt-6-luna |
$0.10 / $0.50* | 1,050,000[17] |
| Claude Fable 5.2 | claude-fable-5-2 |
$8 / $40 | 1,000,000[2] |
| Claude Opus 5.5 | claude-opus-5-5 |
$4 / $20 | 1,000,000[20] |
| Claude Sonnet 5.5 | claude-sonnet-5-5 |
$2 / $10 | 1,000,000[21] |
| Claude Haiku 5.5 | claude-haiku-5-5 |
From $0.10 / $0.50** | 1,000,000[22] |
* OpenAI long-context condition: for the listed GPT models, prompts above 272K input tokens use twice the input/cache rates and 1.5 times the output rate for the full request.[14][15][16]
** Haiku condition: prompts above 100,000 input tokens use $0.50 input and $2.50 output per million tokens. Its lowest displayed price is therefore not the price for every prompt size.[22]
The small difference between the published context capacities is not a quality score. It says little by itself about retrieval accuracy, how much of a repository an agent reads, or whether a long document analysis preserves the important details.
3. A simple cost example is not a project quote
Using the listed base rates, an illustrative request with 100,000 input tokens and 10,000 billable output tokens costs $0.30 on Sol and $1.20 on Fable 5.2 before the excluded adjustments. These figures are arithmetic examples, not measured runs or estimates of equal-quality work.[14][2]
An agent may issue many requests, repeat tests, invoke paid tools, and require human correction. Even a cheaper request can be the expensive choice if the workflow must be repeated. Our recommendation is to measure cost per accepted deliverable, including review time, rather than price per token alone.
4. Apps and API accounts need separate checks
Do not infer a ChatGPT app's model selection, context allocation, or tools from an API model page. Sol's documentation itself illustrates the distinction by listing an app default context separately from its API window.[14]
It is also too simplistic to say a paid app plan either gives unlimited API use or never includes API credit. OpenAI documents API-credit eligibility for particular ChatGPT plans; Anthropic separately documents monthly credits for eligible Max, Ultra, and Team plans. Conditions, activation, usage coverage, and exhaustion rules matter.[23][24]
For a buying decision, check the exact workspace and plan. For an engineering decision, record the actual API model ID, endpoint, tools, and billing basis used in the evaluation.
5. Tooling and migration can change the result
A coding assistant with a repository, test runner, browser, and a long execution window is not equivalent to a plain chat answer. Our proposed comparison keeps those permissions and the acceptance criteria the same wherever practical.
There are also model-specific integration differences. Fable 5.2's migration guide documents conversation-prefix checks, always-on adaptive thinking, and unsupported forced tool-choice settings. A wrapper written for an older model can require changes before a comparison is meaningful.[3]
If a generated animation uses Blender, or a web demo uses Three.js, account for those tools and libraries. A screen recording does not by itself establish that the base language model has native video output.
6. Privacy and specialist access are separate axes
Fable 5.2's official overview specifies a default 30-day retention requirement, with zero-retention use requiring express authorization. That is a model-specific consideration, not a statement that every Claude plan and deployment has one identical policy.[2]
For either provider, review the applicable contract, processing location, retention terms, and tool integrations before uploading confidential files. Do not confuse consumer-app settings with an API or enterprise contract.
GPT-6 Cyber belongs in a separate category: OpenAI documents it for approved security work with restricted access. It should not be presented as an unrestricted upgrade for ordinary ChatGPT tasks. The model reference lists a standard short-context price of $20 input and $100 output per million tokens, with further conditions.[18]
7. Which would we evaluate first?
The following is our proposed shortlist, not a ranking:
- Repository maintenance: compare Sol or Opus on a representative task; add Astra or Fable when the baseline misses important requirements.
- Visual or interface-heavy work: include Terra, and give the Claude candidate equivalent tools and evidence.
- Structured extraction at volume: include Luna and Haiku, while accounting for Haiku's prompt-length tier.
- A difficult unattended project: evaluate Fable or Astra with a firm budget and a human review gate.
- Network change review: begin with synthetic, read-only bundles and score evidence accuracy, missed risks, and invented findings—not confidence of wording.
Original proposed evaluation plan. We did not execute this benchmark for the article.
The fair test
Choose a coding task, a source-grounded document task, and an operational task with known answers. Save the same inputs, define what counts as accepted, and allow repeated runs under a declared budget.
Record failed attempts, not just the best output. Distinguish a model mistake from a missing tool or permission. Keep the resulting artifacts so another engineer can inspect what passed.
Our conclusion is not that one brand wins. The useful choice is the model-and-tool combination that meets your acceptance criteria, data requirements, latency target, and total budget on work you can actually verify.
Sources
Primary sources checked November 12, 2026. Creator and vendor claims are attributed; no independent model benchmark is claimed.
- Introducing Claude Fable 5.2 and Claude Mythos 5.2
- Claude Fable 5.2 model overview
- Fable 5.2 API changes
- Claude model catalog
- Official model comparison source 1
- Official model comparison source 2
- Official model comparison source 3
- Official model comparison source 4
- Official model comparison source 5
- Official model comparison source 6
- Official model comparison source 7
- Official model comparison source 13
- Official model comparison source 14
- Official model comparison source 15
- Official model comparison source 20
- Official model comparison source 21
Related reading: our earlier Claude Fable 5 project roundup covers the previous model generation, not Fable 5.2.
Post a Comment