💰 Unit Cost Winner Claude 3.5 Haiku $0.80/1M in · $4.00/1M out
⚡ Latency & Speed Winner Claude 3.5 Haiku 190ms TTFT · 130 tok/s
💻 Coding (SWE) Winner Grok 4 73.2% SWE-bench · 95% HumanEval
🧠 STEM Reasoning Winner Grok 4 86.2% GPQA · 97.6% MATH-500

Which model is better: Claude 3.5 Haiku or Grok 4?

Claude 3.5 Haiku is 68.0% cheaper for input tokens ($0.80 vs. $2.50 per 1M tokens) and $4.00 vs. $12.50 for output tokens (3.1x cost advantage). In terms of operational performance, Claude 3.5 Haiku delivers lower response latency at 190ms TTFT (290ms faster than Grok 4) with 130 vs. 95 tok/s throughput. Grok 4 leads coding evaluations at 73.2% SWE-bench Verified vs. 40.6% for Claude 3.5 Haiku and 86.2% vs. 41.6% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.

Verified Performance & Unit Economics Delta (12 Dimensions)

Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.

Metric / Dimension Claude 3.5 Haiku Grok 4 Net Delta / Advantage
Input Cost / 1M Tokens $0.80 $2.50 Claude 3.5 Haiku is 3.1x cheaper
Output Cost / 1M Tokens $4.00 $12.50 Claude 3.5 Haiku (68.0% lower)
Prompt Cache Read / 1M $0.080 $0.500 Claude 3.5 Haiku is 6.3x cheaper
Prompt Cache Write / 1M $1.00 $2.50 Claude 3.5 Haiku is 2.5x cheaper
Context Window (Max Output) 205k (8k out) 262k (66k out) Grok 4 (1.3x larger context)
Avg TTFT Response Latency 190ms 480ms Claude 3.5 Haiku (290ms faster)
Generation Speed (Throughput) 130 tok/s 95 tok/s Claude 3.5 Haiku (+35 tok/s faster)
SWE-bench Verified (Coding) 40.6% 73.2% Grok 4 (+32.6% lead)
GPQA Diamond (PhD STEM) 41.6% 86.2% Grok 4 (+44.6% lead)
MMLU-Pro (Multi-Discipline) 71% 89.9% Grok 4 (+18.9% lead)
MATH-500 (Quantitative Logic) 69.4% 97.6% Grok 4 (+28.2% lead)
Inference Value Score (IVR) 52.4 / 100 57.6 / 100 Grok 4 (+5.2 pts)

Interactive Monthly Token Economics & ROI Forecaster

Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.

25.0M Tokens
500K 50M 250M 500M+
5.0M Tokens
100K 25M 75M 150M+
50% Cached
0% (Uncached) 25% 50% (Typical Agent) 95% Max

Estimated Monthly Spend

Claude 3.5 Haiku $31.00

$0.80/1M in · $4.00/1M out

Prompt Cache: $0.080/1M read · $1.00/1M write

Grok 4 $100.00

$2.50/1M in · $12.50/1M out

Prompt Cache: $0.500/1M read · $2.50/1M write

Projected Monthly Cost Reduction
$69.00 / mo
(69.0% lower cost with Claude 3.5 Haiku)
Open Full Matchup Analysis →
A

When to Choose Claude 3.5 Haiku

Choose Claude 3.5 Haiku (Anthropic) for rapid customer-facing chat, sub-agent routing, and real-time code autocomplete — offering a 204k context window, 190ms TTFT, and $0.80/1M input pricing ($0.080/1M cached reads).

B

When to Choose Grok 4

Choose Grok 4 (xAI) for real-time financial/market intelligence, PhD-level STEM problem solving, and autonomous code generation — offering a 262k context window, 480ms TTFT, and $2.50/1M input pricing ($0.500/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: Claude 3.5 Haiku or Grok 4?

Claude 3.5 Haiku is cheaper at $0.80/1M input tokens ($4.00/1M output) compared to $2.50/1M input ($12.50/1M output) for Grok 4.

Which model has lower TTFT latency and higher tok/s throughput: Claude 3.5 Haiku or Grok 4?

Claude 3.5 Haiku delivers faster first-token latency at 190ms TTFT (130 tok/s throughput) compared to 480ms TTFT (95 tok/s) for Grok 4.

How do Claude 3.5 Haiku and Grok 4 compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, Claude 3.5 Haiku scores 40.6% versus 73.2% for Grok 4. On GPQA Diamond graduate-level STEM reasoning, Claude 3.5 Haiku scores 41.6% versus 86.2% for Grok 4 (and 69.4% vs 97.6% on MATH-500).

When should you choose Claude 3.5 Haiku?

Choose Claude 3.5 Haiku (Anthropic) for rapid customer-facing chat, sub-agent routing, and real-time code autocomplete — offering a 204k context window, 190ms TTFT, and $0.80/1M input pricing ($0.080/1M cached reads).

When should you choose Grok 4?

Choose Grok 4 (xAI) for real-time financial/market intelligence, PhD-level STEM problem solving, and autonomous code generation — offering a 262k context window, 480ms TTFT, and $2.50/1M input pricing ($0.500/1M cached reads).

Explore High-Priority Related Model Matchups