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GPT-5 mini (high) vs JT-4.1 Flash 236B A21B: The Ultimate Performance & Pricing Comparison

Deep dive into reasoning, benchmarks, and latency insights.

The Final Verdict in the GPT-5 mini (high) vs JT-4.1 Flash 236B A21B Showdown

The current catalog does not contain complete performance evidence for both models, so this page does not declare an overall winner. Use the available fields as comparison signals and validate the models on your own workload.

Model Snapshot

Key decision metrics at a glance.

GPT-5 mini (high)JT-4.1 Flash 236B A21B
9.0
Reasoning
6.0
2.0
Coding
5.0
2.0
Multimodal
3.0
3.0
Long Context
5.0
$0.688
Blended Price / 1M tokens
$15
P95 Latency
Tokens per second

Machine-readable comparison data

ModelMetricValueUnitSource / snapshot
GPT-5 mini (high)Reasoning9.0benchmark or capability scoreArtificial Analysis · current catalog
JT-4.1 Flash 236B A21BReasoning6.0benchmark or capability scoreArtificial Analysis · current catalog
GPT-5 mini (high)Coding2.0benchmark or capability scoreArtificial Analysis · current catalog
JT-4.1 Flash 236B A21BCoding5.0benchmark or capability scoreArtificial Analysis · current catalog
GPT-5 mini (high)Multimodal2.0benchmark or capability scoreArtificial Analysis · current catalog
JT-4.1 Flash 236B A21BMultimodal3.0benchmark or capability scoreArtificial Analysis · current catalog
GPT-5 mini (high)Long Context3.0benchmark or capability scoreArtificial Analysis · current catalog
JT-4.1 Flash 236B A21BLong Context5.0benchmark or capability scoreArtificial Analysis · current catalog
GPT-5 mini (high)Blended Price / 1M tokens$0.688USD per 1M tokensArtificial Analysis · current catalog
JT-4.1 Flash 236B A21BBlended Price / 1M tokens$15USD per 1M tokensArtificial Analysis · current catalog
GPT-5 mini (high)P95 LatencymillisecondsArtificial Analysis · current catalog
JT-4.1 Flash 236B A21BP95 LatencymillisecondsArtificial Analysis · current catalog
GPT-5 mini (high)Tokens per secondtokens per secondArtificial Analysis · current catalog
JT-4.1 Flash 236B A21BTokens per secondtokens per secondArtificial Analysis · current catalog

Data provided by Artificial Analysis; live values use the current catalog.

Overall Capabilities

This radar chart visually maps the core capabilities (reasoning, coding, math proxy, multimodal, long context) of `GPT-5 mini (high)` vs `JT-4.1 Flash 236B A21B`.

IntelligenceCodingMathMultimodalLong Context
GPT-5 mini (high)JT-4.1 Flash 236B A21B

Benchmark Breakdown

This grouped bar chart provides a side-by-side comparison for each benchmark metric.

GPT-5 mini (high)JT-4.1 Flash 236B A21B

Speed & Latency

Lower time to first token is better; higher tokens per second is better.

Time to First Token · GPT-5 mini (high)
Time to First Token · JT-4.1 Flash 236B A21B
Tokens per Second · GPT-5 mini (high)
Tokens per Second · JT-4.1 Flash 236B A21B
Head to the playground to validate these results yourself

The Economics of GPT-5 mini (high) vs JT-4.1 Flash 236B A21B

Pricing Breakdown

Compare input and output pricing in USD per 1M tokens.

GPT-5 mini (high)JT-4.1 Flash 236B A21B

Real-World Cost Scenario

Per run: 1M input tokens + 250k output tokens

GPT-5 mini (high)$0.75

JT-4.1 Flash 236B A21B$17.5

GPT-5 mini (high) costs $16.75 less per run

Review the complete pricing and packaging strategy

Your Questions about the GPT-5 mini (high) vs JT-4.1 Flash 236B A21B Comparison

Is `GPT-5 mini (high)` a direct replacement for `JT-4.1 Flash 236B A21B`?

The current GPT-5 mini (high) vs JT-4.1 Flash 236B A21B data does not establish a universal replacement. Compare the available metrics, then validate quality, latency, reliability, and cost on your own workload.

For coding, which is better in the `GPT-5 mini (high) vs JT-4.1 Flash 236B A21B` debate?

The benchmark data in our GPT-5 mini (high) vs JT-4.1 Flash 236B A21B comparison shows JT-4.1 Flash 236B A21B has a clear advantage, scoring 52.4 on LiveCodeBench versus 15.6.

How was this `GPT-5 mini (high) vs JT-4.1 Flash 236B A21B` comparison conducted?

Our GPT-5 mini (high) vs JT-4.1 Flash 236B A21B comparison uses published benchmark, pricing, speed, and latency fields provided by Artificial Analysis. Missing values remain unavailable, and the result should be checked against your workload.