AI Model Comparison

Grok 4.3 vs Claude Fable 5

Verdict
Grok 4.3 vs Claude Fable 5: Claude Fable 5 scores higher on the Intelligence Index

Head-to-head specifications

MetricGrok 4.3Claude Fable 5Difference
Intelligence Index38.060.0-36.7%
Coding Index42.276.5-44.8%
Agentic Index24.152.8
Context window1M tokens1M tokens
Blended price ($/1M tokens)$0.52$1.68-69.0%
Output speed (tokens/s)11265+72.3%
AccessProprietary APIProprietary API
  • Claude Fable 5 leads overall capability (Intelligence Index 60.0 vs 38.0).
  • Grok 4.3 is the cheaper model to run at $0.52/1M blended tokens — about 3.2× cheaper.

Verdict: Grok 4.3 or Claude Fable 5?

Our recommendation
Claude Fable 5 takes the overall edge, though Grok 4.3 wins in specific areas worth weighing.

Grok 4.3 advantages

  • Affordability (+69%)
  • Output speed (+42%)

Claude Fable 5 advantages

  • General intelligence (+37%)
  • Coding ability (+45%)
  • Agentic task performance (+54%)

Which should you choose?

  • Choose the Grok 4.3 if you want the lowest cost per token at scale.
  • Choose the Claude Fable 5 if you need the strongest overall reasoning and accuracy.
  • Choose the Grok 4.3 if low latency and fast generation matter for your application.

Value for money

Grok 4.3 offers more intelligence per dollar (2.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.

Grok 4.3 vs Claude Fable 5: which should you choose?

Grok 4.3 — xAI multimodal model with an Intelligence Index of 38, a 1M-token context window and a blended price of $0.52/1M tokens.

Claude Fable 5 — Anthropic multimodal model with an Intelligence Index of 60, a 1M-token context window and a blended price of $1.68/1M tokens.

Grok 4.3 vs Claude Fable 5: Claude Fable 5 scores higher on the Intelligence Index. Claude Fable 5 leads overall capability (Intelligence Index 60.0 vs 38.0). Grok 4.3 is the cheaper model to run at $0.52/1M blended tokens — about 3.2× cheaper.

Capability: intelligence, coding and agentic work

On the composite Intelligence Index the Claude Fable 5 scores 60.0 versus 38.0. For software development, the Coding Index puts Claude Fable 5 ahead (76.5 vs 42.2). On agentic, multi-step tool-use tasks, Claude Fable 5 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.

Context window and speed

The Grok 4.3 accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Grok 4.3 generates faster (112 vs 65 tokens/s), which matters for interactive apps and high-volume pipelines.

Pricing and access

At blended per-token rates, Grok 4.3 is the cheaper model to run ($0.52 vs $1.68 per 1M tokens). Grok 4.3 is proprietary api and Claude Fable 5 is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.

The verdict

Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.

Frequently asked questions

Is the Grok 4.3 better than the Claude Fable 5?

Claude Fable 5 takes the overall edge, though Grok 4.3 wins in specific areas worth weighing. Claude Fable 5 leads overall capability (Intelligence Index 60.0 vs 38.0).

What is the main difference between the Grok 4.3 and the Claude Fable 5?

Claude Fable 5 leads overall capability (Intelligence Index 60.0 vs 38.0). Grok 4.3 is the cheaper model to run at $0.52/1M blended tokens — about 3.2× cheaper.

Which is better value?

Grok 4.3 offers more intelligence per dollar (2.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.

Which should I choose?

Choose the Grok 4.3 if you want the lowest cost per token at scale. Choose the Claude Fable 5 if you need the strongest overall reasoning and accuracy.

Methodology

Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.

MC
Marcus Chen
Hardware & Product Analyst

Marcus benchmarks processors, GPUs, phones and vehicles and maintains normalized performance databases.

MSc Computer Engineering10+ years review experience
✓ Reviewed by Priya Nair, Data Quality Reviewer.
Last updated 2026-07-01
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