Two frontier models shipped three weeks apart. Claude Fable 5.1 landed September 1, 2026; Grok 4.7 landed September 21, 2026. They are not chasing the same target. Fable 5.1 is built around long-horizon reasoning and agents that stay alive for hours. Grok 4.7 is built around coding, agentic work, and price-performance.
The headline numbers diverge hard:
- Grok 4.7: 500K context,
grok-4.7, $2/M input, $6/M output, cached input $0.50/M. - Claude Fable 5.1: 1M context,
claude-fable-5-1, $10/M input, $50/M output, $0.25/M cache reads.
That's 80% cheaper input and 88% cheaper output for Grok at base rates — against half the context window and a benchmark table that splits roughly down the middle. Here's how I read it.
The spec sheet, without the positioning copy
| Grok 4.7 | Claude Fable 5.1 | |
|---|---|---|
| Released | Sep 21, 2026 | Sep 1, 2026 |
| Provider | xAI / SpaceXAI | Anthropic |
| Model ID | grok-4.7 |
claude-fable-5-1 |
| Context window | 500K tokens | 1M tokens |
| Max output | No fixed text limit documented | Up to 128K tokens |
| Input | Text + image | Text + image |
| Output | Text | Text |
| Knowledge cutoff | May 2026 | June 2026 |
| Reasoning controls | low / medium / high / xhigh | Adaptive thinking + effort controls |
| Search tools | Web search + X search | Environment/tool dependent |
| Function calling | Yes | Yes |
| Weights | Closed | Closed |
Grok 4.7 runs on a new, larger base model than Grok 4.6, with a longer RL run weighted toward tasks that take many hours. The release notes also call out better self-verification and long-context management — both are things you feel in agent loops, not in a demo.
Fable 5.1 is aimed at multistep research, document-heavy professional work, and agents that keep operating across extended sessions.
The benchmark table splits down the middle
Everything below is vendor-reported from xAI's launch comparison table. Grok 4.7 is measured at xhigh; Fable 5.1 at max effort. xAI separately flags Grok's DeepSWE number as high effort.
| Benchmark | Grok 4.7 | Fable 5.1 | Gap |
|---|---|---|---|
| CursorBench 4.0 | 46.3% | 51.8% | Fable +5.5 |
| DeepSWE v1.1 | 71.0%* | 70.0% | Grok +1.0 |
| AA Briefcase v1.1 | 1,657 | 1,678 | Fable +21 |
| Terminal-Bench 4.0 | 38.0% | 57.9% | Fable +19.9 |
| Harvey Legal Agent Benchmark | 19.6% | 6.7% | Grok +12.9 |
| HealthBench Professional | 56.7% | 62.1% | Fable +5.4 |
| EEBench | 64.0% | 56.4% | Grok +7.6 |
* flagged as high effort by xAI.
Coding
This is the muddiest part of the comparison, which is exactly why you shouldn't pick from a single number. Fable 5.1 wins CursorBench 4.0 by 5.5 points. Grok 4.7 wins DeepSWE v1.1 by 1 point. Terminal-Bench 4.0 is the blowout: 57.9% vs 38.0%, a 19.9-point gap that maps directly onto how much time a model can spend driving a shell unattended.
My read: repository-level editing is close to a coin flip. Terminal autonomy is not. If your agent lives in a PTY for 45 minutes at a time, that 19.9-point spread is the most load-bearing number in either table.
Knowledge work isn't one capability
The same vendor table has Grok ahead on EEBench (engineering, +7.6) and Harvey Legal Agent (+12.9), while Fable leads HealthBench Professional (+5.4) and edges AA Briefcase v1.1 by 21 points. Law, medicine, engineering, and office automation impose different tool surfaces and different reasoning demands. A model that wins "professional work" as a category doesn't exist.
The token math is where this actually gets decided
| Base pricing | Grok 4.7 | Fable 5.1 |
|---|---|---|
| Input / 1M | $2 | $10 |
| Output / 1M | $6 | $50 |
| Cached input / cache read | $0.50 (below 200K prompt) | $0.25 |
| 10M input | $20 | $100 |
| 10M output | $60 | $500 |
| US regional endpoint premium | +10% | Platform dependent |
Worked example — 10M input tokens plus 2M output tokens, no caching:
- Grok 4.7: $20 input + $12 output = $32
- Fable 5.1: $100 input + $100 output = $200
- Nominal gap: $168 per run
Two caveats that bite in production. First, Grok 4.7's $2/$6 is a base rate; SpaceXAI documents higher-context pricing once a request crosses 200K. Second, Fable's $0.25/M cache reads are irrelevant unless your prompt prefix is actually stable across calls — check your cache hit rate before you believe your own cost estimate. Tool charges, regional premiums, and cache writes are all excluded from the math above.
The only cost metric worth tracking is cost per accepted result. A model that costs 6x per token but eliminates one retry loop plus 20 minutes of human review wins, and a cheap model that never passes your acceptance test is the most expensive option on the board.
Reasoning controls aren't comparable across vendors
Grok exposes low, medium, high, xhigh. Fable uses adaptive thinking with effort controls. The names overlap; the behavior doesn't. Matching "high" to "high" is not a controlled experiment — hold the task, tools, and acceptance criteria constant and let each model pick its own effort level, or you're measuring label semantics.
Context is the other structural gap: 1M vs 500K. That matters for whole-repo work, legal discovery, scientific corpora, and agents carrying long histories. It does not matter for the majority of tasks, and paying 5x input for headroom you never touch is waste.
Tooling on Grok's API includes function calling, web search, X search, and code execution as native tools. X search has no equivalent on the Anthropic side. Fable's tool behavior depends on the Claude environment or application stack you're running it in.
Route instead of choosing
The interesting architecture here isn't picking a winner — it's a default route plus a measurable escalation path. I keep the policy explicit in config so it's reviewable and tunable from production logs:
ROUTES = {
"default": {
"model": "grok-4.7",
"reasoning": "high",
"price": {"in": 2.0, "out": 6.0, "cached_in": 0.50}, # per 1M, <=200K prompt
},
"escalate": {
"model": "claude-fable-5-1",
"price": {"in": 10.0, "out": 50.0, "cache_read": 0.25},
},
}
def escalate(task, attempt):
# measurable triggers only — no vibes-based routing
return (
attempt.tokens_in > 200_000 # past Grok's base-pricing tier / context pressure
or not attempt.passed_acceptance_test
or task.needs_long_terminal_execution
or task.cost_of_error > RETRY_BUDGET
)
def route(task):
attempt = call(ROUTES["default"]["model"], task)
if escalate(task, attempt):
attempt = call(ROUTES["escalate"]["model"], task)
log_attempt(task, attempt) # accepted?, tokens, latency, retries, tool failures
return attempt
If you want a single integration layer across providers rather than two SDKs and two billing dashboards, CometAPI exposes both grok-4.7 and claude-fable-5-1 behind one endpoint — useful when the router above is the actual product decision.
Log the trigger alongside the outcome, or the policy never improves. The metrics that matter per route: accepted-result rate, total tokens, cache hit rate, end-to-end latency, tool-failure count, retries, human correction time.
Benchmark hygiene before you trust any of this
- Version drift. CursorBench 3.2.0 and 4.0 are different tests. Do not compare across them.
-
Effort settings. Grok at
xhighvs Fable at max is not the same knob, and Grok's own DeepSWE row is flaggedhigh. - Harness and tool access. Terminal-Bench scores move with the scaffold, permissions, and safeguard configuration around the model.
- Vendor-reported vs reproduced. Every number above comes from one vendor's table. Use it to form hypotheses, not to ship decisions.
Then run both models on your repositories, with your tools, under your time budget and acceptance criteria, with enough repeated trials to separate model behavior from task variance.
Picking
Grok 4.7 when token cost drives unit economics: high-volume coding assistants, batch knowledge processing, background automation, engineering agents where its domain results line up with your workload, and anything that benefits from native web plus X search.
Claude Fable 5.1 when failure is expensive: multi-hour autonomous coding, terminal-heavy agents, very large repos or document sets, professional agents that hold state across many steps, and workflows where a retry costs more than the inference did.
Neither model is a universal winner, and any article claiming otherwise is selling you a leaderboard instead of a cost model. Start with the default-plus-escalation router, measure cost per accepted task, and let production logs move the boundary.
Originally published at cometapi.com
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