Claude Sonnet 4.6 vs GPT-5.4 Nano: Cost-Quality vs Latency Dominance (2026-03-21)

Metric Summary (Given Data)

Model	Quality Index	Coding Index	Math Index	Blended Price	Output Speed
Claude Sonnet 4.6	44.4	46.4	N/A	$6.00/1M tokens	49 tok/s
GPT-5.4 Nano	44.4	43.9	N/A	$0.46/1M tokens	212 tok/s

Data-backed facts:

• Quality Index tie: both models are 44.4.
• Coding Index edge: Claude Sonnet 4.6 scores 46.4 vs 43.9 for GPT-5.4 Nano (+2.5 points).
• Price gap: GPT-5.4 Nano is $0.46/1M vs Claude’s $6.00/1M → GPT-5.4 Nano is ~13.04× cheaper.
• Latency (output throughput) gap: GPT-5.4 Nano is 212 tok/s vs Claude’s 49 tok/s → GPT-5.4 Nano is ~4.33× faster in output speed.

Quality Analysis (Winner)

• Both Claude Sonnet 4.6 and GPT-5.4 Nano have the same Quality Index: 44.4.
• Therefore, no quality winner exists on the provided metric; they tie exactly.

Verdict (Quality): Tie (44.4 vs 44.4).

Coding Analysis (Winner)

• Claude Sonnet 4.6 leads coding with 46.4 vs 43.9.
• That is a relative lead of 46.4 / 43.9 - 1 ≈ 5.69% in Coding Index.

Verdict (Coding): Claude Sonnet 4.6 wins by +2.5 coding points.

Inference Economics: Cost per Quality Point (Decisive)

To compare cost-effectiveness using only provided metrics, compute price per Quality Index point:

• Claude: $6.00 / 44.4 ≈ $0.1351 per quality point per 1M tokens
• GPT-5.4 Nano: $0.46 / 44.4 ≈ $0.01036 per quality point per 1M tokens

Ratio:

• $0.1351 / $0.01036 ≈ 13.04× (matches the raw price ratio because Quality is tied at 44.4)

Conclusion: For the same Quality Index (44.4), GPT-5.4 Nano delivers ~13× lower blended cost per quality point.

Verdict (Economics for Quality): GPT-5.4 Nano wins by ~13.04×.

Latency Analysis (Throughput) + Chart

Latency here is represented by Output Speed (tok/s), which directly impacts how quickly the model streams generated tokens.

• Claude: 49 tok/s
• GPT-5.4 Nano: 212 tok/s
• Throughput ratio: 212 / 49 ≈ 4.33×

Verdict (Latency/Throughput): GPT-5.4 Nano wins by ~4.33× output speed.

Deployment Scenarios (Choose a Winner per Use Case)

1) Coding-heavy (engineering productivity)

• Coding Index favors Claude: 46.4 vs 43.9 (+2.5 points).
• If your bottleneck is coding quality on the given metric, Claude Sonnet 4.6 is the better fit.

Winner (Coding): Claude Sonnet 4.6.

2) General-purpose assistants / mixed tasks

• Quality is a tie: 44.4 vs 44.4.
• With Quality tied, the decision collapses to economics and throughput: GPT-5.4 Nano is ~13× cheaper and ~4.33× faster.

Winner (General): GPT-5.4 Nano.

3) Budget-constrained deployments

• Blended price is the strongest signal: $0.46/1M vs $6.00/1M → ~13× cheaper.
• Since Quality is tied, cost advantage directly translates into more budget per unit of quality.

Winner (Budget): GPT-5.4 Nano.

4) Enterprise scale (cost + throughput matter)

• At enterprise scale, tokens/sec throughput and blended price dominate capacity planning.
• GPT-5.4 Nano offers ~4.33× output throughput and ~13× lower blended price, with equal Quality Index (44.4).

Winner (Enterprise): GPT-5.4 Nano.

Final Verdict (Overall Winner)

Claude Sonnet 4.6 wins only one category: coding quality (46.4 vs 43.9).
GPT-5.4 Nano wins the categories that dominate real deployment value here: cost and throughput ($0.46 vs $6.00 and 212 tok/s vs 49 tok/s) while tying overall Quality (44.4 vs 44.4).

Overall Winner: GPT-5.4 Nano

• Same Quality Index (44.4).
• ~13.04× cheaper for the same quality point.
• ~4.33× faster output throughput.
• Coding quality advantage goes to Claude, but it does not overcome the cost/latency dominance given a quality tie.

Recommendation

• Choose GPT-5.4 Nano for production assistants, enterprise deployments, and any workload where latency and cost are primary constraints.
• Choose Claude Sonnet 4.6 if your priority is maximizing coding quality per the provided Coding Index metric, and you accept the higher $6.00/1M blended price.

To validate fit across more models and metrics, use the LLM Selector or browse directly at Explore.