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OpenAI: GPT-5.1-Codex-Max vs Amazon: Nova 2 Lite: Coding Performance with 10 Evaluators

We analyze the coding capabilities of OpenAI: GPT-5.1-Codex-Max vs Amazon: Nova 2 Lite through a rigorous evaluation conducted by 10 expert evaluators.

OpenAI: GPT-5.1-Codex-Max

7.0

/ 10

vs

Amazon: Nova 2 Lite

3.0

/ 10

Key Findings

Coding AccuracyOpenAI: GPT-5.1-Codex-Max

Scored 6.97 vs 3.03, demonstrating higher precision in code generation.

Instruction AdherenceOpenAI: GPT-5.1-Codex-Max

Successfully followed complex coding constraints with higher reliability.

Economic EfficiencyAmazon: Nova 2 Lite

Offers significantly lower cost per request for lighter workloads.

Specifications

SpecOpenAI: GPT-5.1-Codex-MaxAmazon: Nova 2 Lite
Provideropenaiamazon
Context Length400K1.0M
Input Price (per 1M tokens)$1.25$0.30
Output Price (per 1M tokens)$10.00$2.50
Max Output Tokens128,00065,535
Tierpremiumstandard

Our Verdict

OpenAI: GPT-5.1-Codex-Max stands out as the clear winner in coding performance, providing significantly higher accuracy and instruction following capabilities. While Amazon: Nova 2 Lite is remarkably more cost-effective, it lacks the depth required for complex programming tasks. Organizations prioritizing output quality should opt for GPT-5.1-Codex-Max.

Overview

In the rapidly evolving landscape of LLM development, selecting the right model for software engineering tasks is critical. This comparative analysis focuses on OpenAI: GPT-5.1-Codex-Max vs Amazon: Nova 2 Lite, evaluating their proficiency in coding tasks. By utilizing PeerLM's expert evaluation suite, we provide a transparent look at how these models handle complex instruction following and code accuracy.

Benchmark Results

The evaluation, conducted by 10 independent evaluators, highlights a significant performance gap between the two contenders. OpenAI's flagship model demonstrates superior depth in logic and syntax, whereas the Nova 2 Lite model serves a different segment of the market focused on lightweight, budget-conscious deployment.

ModelOverall ScoreAccuracyInstruction Following
OpenAI: GPT-5.1-Codex-Max6.976.976.97
Amazon: Nova 2 Lite3.033.033.03

Criteria Breakdown

Our evaluation focused on two primary pillars: Accuracy and Instruction Following. In coding scenarios, these metrics determine whether a model produces functional, bug-free code that adheres to specific developer constraints.

  • Accuracy: OpenAI: GPT-5.1-Codex-Max consistently outperformed the competition, showing a deeper understanding of complex syntax and edge-case handling.
  • Instruction Following: The ability to adhere to strict coding standards and formatting requirements was significantly higher in the OpenAI model, which maintained a score of 6.97 compared to 3.03 for Amazon: Nova 2 Lite.

Cost & Latency

When comparing OpenAI: GPT-5.1-Codex-Max vs Amazon: Nova 2 Lite, one must weigh performance against operational overhead. While the OpenAI model commands a higher price per token, the performance ceiling is markedly higher for complex enterprise applications.

ModelAvg Latency (ms)Total Cost (USD)Avg Completion Tokens
OpenAI: GPT-5.1-Codex-Max10280.035294856
Amazon: Nova 2 Lite00.001910162

Use Cases

OpenAI: GPT-5.1-Codex-Max is ideally suited for high-stakes development environments, such as building complex backend systems, automated refactoring, and generating large-scale unit tests where accuracy is non-negotiable. Amazon: Nova 2 Lite, with its significantly lower cost profile, is better positioned for high-throughput, simple scripting tasks or rapid prototyping where minor imperfections are acceptable in exchange for extreme cost efficiency.

Verdict

The evaluation makes it clear that for high-fidelity coding tasks, OpenAI: GPT-5.1-Codex-Max is the superior choice. While Amazon: Nova 2 Lite offers a compelling price point, its current performance level is best reserved for low-complexity scripting rather than core software engineering workflows.

Backed by real data

View the Full Evaluation Report

See every response, score, and evaluator judgment behind this comparison. All data from PeerLM's blind evaluation pipeline.

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Methodology

Evaluated using PeerLM's blind evaluation pipeline with 4 responses per model across 2 criteria.