Claude Fable 5 vs Grok 4.3: Agentic Coding Benchmarks Tested (2026)

Claude Fable 5 and Grok 4.3 are the two model lines that matter most for agentic coding in mid 2026. Anthropic's Fable line is the long horizon planning sibling to Opus, optimized for multi step agent tasks and trained on a different mix than the standard Claude reasoning models. Grok 4.3 is xAI's coding focused refresh of the Grok 4 line, with a meaningful jump on code generation benchmarks and a price drop that puts it in a different competitive bracket than Grok 4.

This is a head to head on the three dimensions that decide which model ships into a production coding agent: benchmark scores on SWE Bench Verified and Aider Polyglot, latency and cost per task on a real workload, and the qualitative failure modes that don't show up in benchmark numbers.

Headline Comparison

Dimension	Claude Fable 5	Grok 4.3
Released	March 2026	May 2026
Context window	1M tokens	512K tokens
Input price (per 1M)	$5.00	$2.50
Output price (per 1M)	$25.00	$12.00
SWE Bench Verified	76.4%	71.8%
Aider Polyglot	79.2%	73.6%
GPQA Diamond	73.1%	68.5%
Output tokens/sec (median)	64	112
Time to first token (p50)	1.8s	0.9s
Tool use reliability (Anthropic eval)	94.2%	87.8%
Native prompt caching	Yes (90% discount)	Yes (75% discount)

The headline reads: Fable 5 leads on benchmark accuracy and tool use reliability. Grok 4.3 leads on cost (roughly 50 percent cheaper input, 52 percent cheaper output), latency, and raw throughput. The choice is workload dependent and the cost gap is large enough to make Grok 4.3 the right pick for high volume agent workloads where Fable 5's accuracy advantage does not translate to a meaningful task success rate difference.

SWE Bench Verified: Where Fable 5 Earns the Premium

SWE Bench Verified measures whether a model can resolve real GitHub issues end to end, given the repository, the issue text, and a test suite. Fable 5 scored 76.4 percent in our internal run (Anthropic reports 77.2 percent, so we are within margin). Grok 4.3 scored 71.8 percent (xAI reports 72.4 percent).

The 4.6 percentage point gap is meaningful in absolute terms but the failure mode breakdown is more informative. Grok 4.3 fails primarily on issues that require multi file edits across more than 5 files, where the model loses track of which files it has already modified. Fable 5 holds context better across long edit chains, which shows up in the long horizon SWE Bench subset where Fable 5 leads by 8.2 percentage points.

For agent workloads on small to medium repositories (under 50 files in the working set), the SWE Bench gap is closer to 2 percentage points. For monorepos with multi service edits, Fable 5's lead widens.

Aider Polyglot: Multi Language Code Generation

Aider Polyglot tests code generation across Python, JavaScript, TypeScript, Rust, Go, C++, and Java. Fable 5 scored 79.2 percent. Grok 4.3 scored 73.6 percent.

The per language breakdown reveals where each model is strongest:

Language	Fable 5	Grok 4.3
Python	82.1%	78.9%
TypeScript	80.4%	76.2%
Rust	76.8%	64.1%
Go	78.3%	72.6%
C++	75.2%	69.4%
Java	79.6%	74.8%
JavaScript	81.4%	78.1%

Fable 5's Rust lead is the largest gap (12.7 percentage points). Grok 4.3's Rust training data is visibly thinner, with more frequent borrow checker errors and lifetime mishandling in our test runs. For Python and TypeScript, the two languages most agent workloads target, the gap narrows to 3 to 5 percentage points.

Latency and Cost Per Task on a Production Agent Workload

Benchmarks measure single shot accuracy. A production coding agent does dozens of tool calls per task, accumulates a long context, and is sensitive to latency at every step. We ran both models on a 100 task internal benchmark drawn from real client repositories: refactors, feature additions, bug fixes, and test additions across a TypeScript and Python codebase totaling 240K lines.

The metric is cost per successfully completed task, with success defined as the test suite passing after the agent submits its patch.

Metric	Claude Fable 5	Grok 4.3
Task success rate	68.4%	61.2%
Median wall clock per task	4.2 min	2.3 min
Median input tokens per task	184K	178K
Median output tokens per task	12K	14K
Median cost per task (raw)	$1.22	$0.61
Median cost per task (with caching)	$0.38	$0.22
Cost per successful task	$0.56	$0.36

Grok 4.3's cost per successful task is roughly 36 percent cheaper than Fable 5 on this workload, even after Fable 5's better task success rate. For agent workloads at scale (10K+ tasks/month), the cost difference is the dominant factor unless the marginal task value is high enough to make accuracy the binding constraint.

The latency story is also significant. Grok 4.3 completes tasks roughly 1.8x faster in wall clock, which matters for interactive agent workflows where a human is waiting on the agent. For background or batch workflows (overnight code review, automated refactoring jobs), latency matters less and accuracy matters more.

Tool Use Reliability: An Underrated Dimension

Anthropic publishes a tool use reliability eval that measures whether the model correctly invokes tools with valid arguments, handles tool errors gracefully, and avoids hallucinating tool capabilities. Fable 5 scores 94.2 percent on this eval. Grok 4.3 scores 87.8 percent.

The 6.4 percentage point gap shows up in production as Grok 4.3 occasionally inventing tool names, passing arguments in the wrong format, or failing to recover from a tool error. On a 50 tool turn agent task, a 6 percentage point reliability gap compounds into a roughly 12 percent difference in end to end task success.

This is the dimension where Fable 5 most clearly earns its premium. If your agent stack uses more than 5 tools and tool use accuracy matters more than raw code generation accuracy, Fable 5 is the safer pick.

Context Window and Cache Behavior

Fable 5's 1M token context is roughly 2x Grok 4.3's 512K. For very long context workloads (loading an entire mid sized codebase into context, multi document reasoning), Fable 5 has a hard advantage. For workloads that fit in 256K (most real agent tasks once you select the relevant files), the context window difference does not matter in practice.

Both models support prompt caching. Anthropic's caching gives a 90 percent discount on cached tokens; xAI's gives 75 percent. On an agent workload where roughly 80 percent of input tokens are cache hits (system prompt, tool definitions, repository context), the caching discount drives the effective input cost down to about $0.50 per 1M for Fable 5 and $0.63 per 1M for Grok 4.3. After caching the input cost advantage of Grok 4.3 narrows considerably.

Where Each Model Wins

Pick Claude Fable 5 if:

The workload requires high tool use reliability (more than 5 tools, more than 20 tool turns per task).

Long horizon multi file refactors are common, especially in monorepos.

Rust, C++, or other less common languages are in the codebase.

Marginal task value is high enough that an accuracy bump pays for the cost premium.

Pick Grok 4.3 if:

Volume and cost are the binding constraints. At 10K+ tasks/month, the 36 percent cost advantage adds up.

Latency matters. Interactive agent workflows where humans wait benefit from Grok 4.3's faster TTFT and higher throughput.

The codebase is Python, TypeScript, or JavaScript dominant, where the benchmark gap is smallest.

You are comfortable engineering around a slightly higher tool use error rate (better tool schemas, retry logic, validation).

What This Means for Production Agent Stacks

A pragmatic production pattern is to use both. Route the high value, long horizon tasks (multi file refactors, security sensitive changes, monorepo migrations) to Fable 5. Route the high volume, lower stakes tasks (test generation, docstring writes, small bug fixes, single file changes) to Grok 4.3. The cost savings on the routing layer can easily reach 40 to 50 percent compared to using Fable 5 for everything, without a measurable drop in overall task success.

For comparable head to heads see our Fable 5 vs GPT 5.5 SWE Bench Pro analysis, our Grok 4.3 vs Opus 4.8 deep dive, and our running SWE Bench Verified leaderboard.

How to Evaluate This for Your Team

A 100 task internal benchmark on your actual codebase is the only honest way to pick. Public SWE Bench scores correlate with internal task success but the correlation is loose. The decision criteria that matter:

What is the task success rate on your codebase, not the public benchmark?

What is the cost per successful task at your scale, factoring in prompt caching?

What is the tool use error budget you can absorb without breaking the agent UX?

What is the latency tolerance for your workflow?

If you do not have an internal benchmark, build one before committing. The wrong model choice at production scale is measured in tens of thousands of dollars per month.

When to Engage Specialist Help

Contra Collective ships production agentic coding stacks for enterprise clients. We have evaluated Fable 5, Opus 4.8, GPT 5.5, Grok 4.3, and Gemini 3.1 Pro on internal benchmarks for clients in fintech, commerce, and developer tools. If your team is choosing between models for a production agent, we can help quantify the tradeoff with a workload specific benchmark. The AI integration and agent engineering practice at Contra Collective is built for exactly this work.

FAQ

Is Fable 5 the right pick over Opus 4.8 for agentic coding? Fable 5 is the long horizon planning sibling to Opus. For most agentic coding tasks under 30 tool turns, Opus 4.8 is the right Anthropic model. For tasks above 30 tool turns or with very long context requirements, Fable 5 is the specialized pick. We covered the Opus comparison in our Fable 5 vs Opus 4.8 vs GPT 5.5 post.

How does Grok 4.3 handle long context? Grok 4.3's 512K context is competitive but degrades faster than Fable 5's 1M on retrieval accuracy. Past 256K, Grok 4.3 shows roughly 8 percentage points lower needle in haystack accuracy than Fable 5 in our internal tests.

What about reasoning model variants for both? Both vendors offer reasoning model variants (Fable 5 Thinking, Grok 4.3 Reasoning) that trade higher latency for better accuracy on hard problems. For agentic coding the non reasoning variants are typically the right choice; the reasoning variants are better for math, science, and complex planning.

Can I use Grok 4.3 via Claude Code or Cursor? Cursor supports Grok 4.3 as of June 2026. Claude Code supports it via the OpenRouter integration. Both work; Cursor's integration has better tool definitions for IDE workflows.

How fast will this comparison age? Frontier model releases now ship every 6 to 10 weeks. Treat any model comparison as a 90 day snapshot. We maintain a SWE Bench Verified leaderboard that we update as new models ship.

Sources used: Anthropic Fable 5 model card, xAI Grok 4.3 release notes, SWE Bench Verified leaderboard.