My starting policy would be GPT-5.6 Sol for routine frontier workloads, with Claude Fable 5.1 available for difficult autonomous tasks. I’d keep that routing policy only if it beats a single-model baseline on cost per successful completion.
The benchmark evidence favors Fable for demanding repository work and long-running agents. Sol has lower ordinary token prices, a broad native tool stack, and lower measured startup latency in the cited maximum-effort comparison. Those advantages matter differently depending on whether I’m building an interactive assistant or an asynchronous agent.
The figures below come from the source’s September 8, 2026 snapshot, except where another date is specified. Prices are USD per million tokens, abbreviated MTok. Vendor evaluations and independent tests use different setups; their scores need to stay separate.
Start with the workload and API contract
Before comparing benchmark scores, I’d check whether each model fits the application’s execution loop.
| Capability | GPT-5.6 Sol | Claude Fable 5.1 |
|---|---|---|
| Context window | 1.05M tokens | 1M tokens |
| Maximum output | 128K tokens | 128K tokens |
| Text and image input | Supported | Supported |
| Reasoning configuration | Six effort levels, none through max | Always-on adaptive thinking |
| Default effort | Medium | High |
| Forced tool selection | Supported; check the chosen API | Restrictions apply; check supported modes |
Sol’s reasoning controls give me several settings to test against quality, latency, and cost. Fable’s adaptive thinking uses effort to control reasoning depth, but thinking remains enabled.
That makes a default-versus-default comparison awkward: medium Sol and high Fable are different configurations. Identical effort labels also do not establish identical compute budgets. I’d compare the exact settings intended for deployment, or equivalent budgets where those can be established.
Sol’s runtime can change the integration cost
GPT-5.6 Sol targets demanding coding, research, planning, and agent workflows. Through the Responses API, its documented tool portfolio includes:
- Web search and file search
- Code interpreter and hosted shell
- Apply patch and computer use
- MCP, skills, and tool search
OpenAI also describes programmatic tool calling and multi-agent execution for the GPT-5.6 family.
For an application that already needs search, shell execution, file access, and patches, that coverage can reduce the number of runtime components I have to integrate. I would count that engineering cost alongside inference spending.
Fable needs an evaluation that exercises autonomy
Fable 5.1 targets coding, professional knowledge work, research, and long-running agents. Anthropic emphasizes sustained execution across applications: planning, recovering from failed steps, and communicating progress.
I’d translate that positioning into tests with intermediate failures and multiple opportunities to recover. A clean answer to a short prompt tells me little about whether an agent can finish a repository change after its first test run fails.
I’d also test Fable’s forced tool_choice restrictions before migrating a deterministic loop. A workflow that requires a specific tool call needs an explicit compatibility check.
Read the benchmark evidence in three separate groups
The chronology matters. OpenAI’s original GPT-5.6 launch evaluations predate Fable 5.1. Anthropic’s later release includes direct comparisons, while Artificial Analysis provides an independent view.
Independent results: Fable leads overall, Sol has a computer-use advantage
Artificial Analysis introduced Intelligence Index v4.3 on September 7. It replaced Terminal-Bench 2.1 with Terminal-Bench 4.0 and added AutomationBench-AA.
The release reported Intelligence Index scores of 53 for Fable and 47 for Sol, matching the rounded September 8 comparison.
| Independent metric | GPT-5.6 Sol | Claude Fable 5.1 |
|---|---|---|
| Intelligence Index v4.3, September 8 | 47 | 53 |
| Terminal-Bench 4.0, September 7 release | 39.9% | 52.0% |
| OSWorld 2.0 | 62.6% | 41.7% |
| Output throughput, September 8 | 69.8 tokens/s | 69.9 tokens/s |
| Time to first token, September 8 | 132.10 s | 277.47 s |
| AA normalized token-mix price / MTok | $3.08 | $7.175 |
The comparison snapshot uses Sol at max effort and Fable with Adaptive Reasoning, Max Effort, Default Fallback. Live measurements can change.
Fable’s higher Intelligence Index and Terminal-Bench scores support testing it on difficult autonomous work. Sol’s OSWorld 2.0 result is a substantial counterpoint: 62.6% versus 41.7%, a 20.9-percentage-point lead.
I would avoid turning the overall index into a universal ranking. A computer-use application and a terminal coding agent may favor different models.
The normalized price also needs context. Artificial Analysis uses a 7:2:1 cache-hit/input/output ratio. Its $3.08 and $7.175 figures describe that mix, rather than every request an application might send.
Anthropic’s direct comparison favors Fable across all five shared tasks
Anthropic’s published benchmark graphic includes five benchmarks with scores for both models.
| Benchmark | GPT-5.6 Sol | Claude Fable 5.1 | Fable lead |
|---|---|---|---|
| Terminal-Bench-Science 0.1 | 22.4% | 52.6% | 30.2 percentage points |
| Terminal-Bench 4.0 | 37.3% | 55.8% | 18.5 percentage points |
| GDPval-AA v2 | 1711 Elo | 1853 Elo | 142 Elo |
| AutomationBench | 19.6% | 31.4% | 11.8 percentage points |
| CursorBench 3.2.0 | 67.2% | 73.4% | 6.2 percentage points |
The consistency is useful evidence: Fable leads every shared row. The largest gaps appear in scientific agent work and terminal coding.
These remain vendor-run tests. Anthropic evaluated both its own model and the competitor. It reports a ±3.5–4.5-percentage-point standard error per model on Terminal-Bench-Science; the table contains point estimates, not confidence intervals.
The independent terminal result points in the same direction: 52.0% versus 39.9%. I would not merge that with Anthropic’s 55.8% versus 37.3%, because the evaluation setups differ.
OpenAI’s launch score belongs to an older benchmark
OpenAI reports 88.8% on Terminal-Bench 2.1 for GPT-5.6 Sol.
That supports Sol’s coding capability under the launch evaluation. It does not provide a numerical comparison with Terminal-Bench 4.0 or a direct head-to-head result against Fable 5.1.
For my coding evaluation, I’d use repository-wide edits, runnable tests, review tasks, and performance work. Both models can handle many straightforward code-generation requests; the useful separation appears when they must inspect files, execute commands, diagnose failures, and iterate.
Separate startup delay from generation speed
The maximum-effort measurements show essentially equal output throughput:
- Sol: 69.8 tokens/s
- Fable: 69.9 tokens/s
Time to first token differs much more:
- Sol: 132.10 seconds
- Fable: 277.47 seconds
I would describe Sol as starting visible output sooner in this configuration. Calling it universally faster would go beyond the evidence.
Maximum-effort reasoning can consume substantial time before the first visible token. Prompt length, effort, provider load, tools, and cache state can all change the result. Time to first token also differs from total task duration.
For an interactive coding assistant, I’d test lower effort settings and the intended concurrency. For overnight repository work, I’d give successful completion more weight than startup delay. In both cases, I’d measure the full workflow, including tool execution and retries.
Price the session, including cache creation
At the cited OpenAI and Anthropic rates, Sol has a clear advantage on fresh input and output.
| Direct-provider price component | GPT-5.6 Sol | Claude Fable 5.1 |
|---|---|---|
| Input / MTok | $4.00 | $10.00 |
| Output / MTok | $20.00 | $50.00 |
| Cache read / MTok | $0.40 | $0.25 |
| Cache write / MTok | $5.00, 30-minute retention | $12.50, 5-minute retention |
| Above 272K input: input / cache read / cache write / output | $8 / $0.80 / $10 / $30 | Same documented base rates |
Fable costs 2.5× as much for ordinary uncached input and output. Its short-context cache reads are 37.5% cheaper, calculated as ($0.40 − $0.25) / $0.40.
Those cache-write prices purchase different retention periods. Sol’s documented default is 30 minutes; the Fable price shown is for a five-minute write. Cache creation, refresh, and expiration behavior can change the session total.
For a short-context request with 100K input tokens and 10K total billed output tokens, direct-provider costs are:
| Model | Calculation | Cost |
|---|---|---|
| Sol | 0.1 × $4 + 0.01 × $20 |
$0.60 |
| Fable | 0.1 × $10 + 0.01 × $50 |
$1.50 |
These estimates exclude cache writes and tool fees.
A unified multi-model API can simplify testing both routes: CometAPI lists both models with native request formats, and the source’s gateway rates are $3.20/$16 input/output per MTok for Sol and $8/$40 for Fable, making that same request $0.48 or $1.20 respectively. I’d still verify route-specific reasoning, caching, and tool behavior before using either route for an agent.
Million-token workloads change the arithmetic
Sol’s 1.05M-token context is 50,000 tokens larger than Fable’s 1M window, a 5% difference. Both support up to 128K output and text/image input.
For most architectures, I’d investigate billing and context reuse before making that capacity difference decisive.
Sol applies higher request rates when input exceeds 272K tokens:
- Input: $8/MTok
- Cached input: $0.80/MTok
- Cache writes: $10/MTok, up from $5
- Output: $30/MTok
Fable retains its documented 1M-context rates of $10 input, $50 output, and $0.25 cache reads per MTok, without a separate premium above 272K in this configuration.
Consider one turn containing:
- 100K uncached input tokens
- 900K cache-read tokens
- 20K total billed output tokens
| Model | Calculation | Turn cost |
|---|---|---|
| Sol | 0.1 × $8 + 0.9 × $0.80 + 0.02 × $30 |
$2.12 |
| Fable | 0.1 × $10 + 0.9 × $0.25 + 0.02 × $50 |
$2.225 |
The gap nearly disappears because Fable’s cheaper cache reads offset much of its higher fresh-input and output pricing.
This example assumes the 900K-token prefix is already cached and the 100K fresh input is not charged as a new cache write. It excludes the initial cache write and tool fees. The 20K output allowance includes all billed output, including billed reasoning tokens.
I would use this as a reason to model the entire session. It does not establish equal session costs: write frequency, expiration, fresh input, and output volume still matter.
Evaluate agent behavior beyond benchmark accuracy
For autonomous coding, Fable has the stronger current evidence. It leads both cited Terminal-Bench 4.0 comparisons, and Anthropic’s CursorBench 3.2.0 result favors it 73.4% to 67.2%.
For agents built around search, files, shell execution, patches, and MCP, Sol’s native tool coverage deserves its own evaluation. A capable model with a runtime that fits the application may require less orchestration work.
Here is how I’d prioritize testing:
| Workload | Starting candidate | What I’d verify |
|---|---|---|
| Difficult autonomous repository changes | Fable | Completion rate, tests, recovery after failures |
| Long-running research | Fable | Persistence and successful completion |
| High-volume frontier inference | Sol | Quality at the required cost |
| Interactive coding | Sol | Latency at deployed effort and concurrency |
| Agent using many native tools | Sol | Tool success and integration requirements |
| Computer-use workflow | Sol | Whether the OSWorld advantage transfers |
| Repeated million-token context | Both | Full cache lifecycle and session cost |
| Image or document reasoning | Both | Accuracy on the actual workload |
| Existing OpenAI application | Sol | Whether migration adds enough value |
Anthropic’s emphasis on progress communication and sustained autonomy makes Fable worth testing on long jobs. OpenAI’s multi-agent platform features make Sol attractive where coordination is already part of the design. Neither removes the need to measure tool behavior and recovery.
Sensitive workloads have additional observable behavior
Both providers describe stronger safeguards for sensitive cybersecurity and scientific capabilities.
OpenAI documents layered protections and monitoring. Anthropic’s Fable terms describe rerouting some sensitive cybersecurity or biology requests to less capable models, without charging the Fable rate for those requests.
Anthropic also specifies a 30-day default data-retention period, with exceptions for eligible enterprise arrangements.
For security products, regulated workloads, or privacy-sensitive deployments, I’d include those behaviors in the application evaluation. Ordinary coding tests may never exercise them.
Run an evaluation that can justify routing
I’d begin with a fixed task set and native-format requests for each model. Prompts, datasets, concurrency, and scoring rules should remain stable. Effort configurations need to be recorded explicitly, since the APIs expose different controls.
A single sequential request to each model is insufficient for estimating quality or latency. I’d repeat runs in alternating order, establish a common baseline, then tune each model separately.
For every task, I’d record:
- Correctness and test pass rate
- Tool success and recovery from failed steps
- Retries and total token usage
- Time to first token and total elapsed time
- Total cost per successful task
The useful production comparison is between complete policies: Sol alone, Fable alone, and Sol with escalation to Fable. Escalation adds another attempt, so its extra successful completions must justify the additional spending and delay.
My default would remain Sol for fresh-context traffic and applications that benefit from its integrated tools. I’d prioritize Fable when difficult autonomous work determines the product’s success rate. For heavily cached sessions or interactive use, I’d let measurements from the deployed configuration decide.
A router earns its place when it improves completed-task economics at the required quality. If one model already meets those requirements, I’d keep the implementation simple.
Originally published at cometapi.com
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