GPT-5.1 Codex vs Claude Code: the benchmark that actually matters

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If you spend your days writing code, you'll have noticed that lately there's a veritable avalanche of AI models for programmingGPT-5.1 Codex, GPT-5 Codex, Claude Code, Kimi K2 Thinking, Sonnet 4.5, Haiku… The list grows almost every week, and each vendor claims to have the best development assistant. But when you get down to brass tacks and use them on real projects, the differences become very clear.

In recent weeks several teams have been comparing GPT-5.1 Codex, GPT-5 Codex, Claude Code and Kimi K2 Thinking Under rather demanding conditions: large repositories, integration with real pipelines, load testing, and complex observability issues. No simplistic programming katas here, but rather bugs and features that could break production if they go wrong. From all this material emerges a rather compelling message: OpenAI's Codexes, and specifically the GPT-5.1 Codex, are delivering the most "actually deployable code."

GPT-5.1 Codex vs Claude Code: A quick overview of the duel

When someone talks about “GPT-5.1 Codex vs Claude Code benchmark”, they are actually comparing two quite different philosophies of code assistantGPT-5.1 Codex (and its evolution GPT-5.1-Codex-Max) is designed from the outset as an engine for agents that work many hours on the same repository: it understands the context, edits files, runs tests, and corrects its own errors. Claude Code, on the other hand, excels at explaining code, designing architectures, and generating documentation, but it often falls short when it comes to truly integrating changes into an existing codebase.

In real-world tests with observability projects, this difference was clearly seen: Codex models were the only ones that generated integrated, production-ready code.While Claude and Kimi produced flashy architectures, creative ideas and lots of lines… but with critical bugs, integration failures or simply code that wouldn't even compile.

How the benchmark was done: real problems, not toys

To make the benchmark meaningful, the typical "write a function that reverses a string" exercise was completely avoided. Instead, the following were selected: two complex challenges within an observability platformwith very specific performance and reliability requirements, and following best practices of testing and implementation in software engineering:

First challenge: design and implement a system of statistical detection of anomalies Capable of learning baseline error rates, calculating z-scores and moving averages, detecting spikes in the rate of change, and handling over 100.000 logs per minute with less than 10 ms of latency. All of this integrated into an existing pipeline.

Second challenge: solve the distributed alert deduplication When multiple processors detect the same anomaly almost simultaneously, it was necessary to avoid duplicates with less than 5 seconds between them, tolerate clock lags of up to 3 seconds, and handle processor crashes without leaving the system frozen.

The four models tested —GPT-5 Codex, GPT-5.1 Codex, Claude Code and Kimi K2 ThinkingThey received the same prompts, in the same IDE (Cursor), and from the same repository. Measurements were taken. time spent, tokens consumed, cost in dollars, code quality, number of critical bugs And, very importantly, whether the result was truly connected to the existing codebase or remained a "parallel prototype".

Test 1 Results: Statistical detection of anomalies

In the first test, the goal was for each model to deliver a production-ready statistical anomaly detector: rate calculations, sliding windows, z-scores, change spikes, careful handling of division by zero, and integration into the class AnomalyDetector and in the actual pipeline.

Claude Code It was launched with a bang: thousands of new lines of code, extensive documentation, several statistical mechanisms (z-score, EWMA, exchange rate checks), and even synthetic benchmarks. On paper, it sounded like textbook engineering. But when the code was run, the flip side appeared: an exchange rate function that returned Infinity when the previous window was zero, and then a toFixed() about that value that caused a Immediate RangeErrorFurthermore, the baseline system was not truly rolling, and the tests were non-deterministic (using Math.random()And to top it all off, None of this was connected to the actual pipelineResult: a striking prototype, but impossible to put into production as is.

The attempt to GPT-5 Codex It was much more pragmatic. In about 18 minutes it generated well-integrated code, with net changes of only a few hundred lines, directly on the class AnomalyDetector and the actual entry points. They took care to handle edge cases (for example, Number.POSITIVE_INFINITY before calling toFixed()), implemented incremental statistics in rolling windows with O(1) complexity and aligned the time buckets with the wall clock for predictability. Unit tests They were deterministic and the result ran in the system without touching almost anything else.

As for the GPT-5.1 CodexHe took an even cleaner architectural approach. Instead of temporary buckets, he used sample-based rolling windows with head/tail pointers and a dedicated class. RollingWindowStats to perform sums and sums of squares. He carefully controlled division by zero using constants such as MIN_RATE_CHANGE_BASE_RATEHe limited the baseline update frequency to save resources and wrote deterministic tests with controlled timestamps. In 11 minutes it produced more net lines than GPT-5 but with a simpler architecture, better memory management and the same "deploy-ready" quality.

The fourth player, Kimi K2 ThinkingThey opted for a creative solution that combined streaming log support and batch metrics, adding detections based on MAD and EMA. On paper, it didn't look bad, but the core was broken: it updated the baseline before evaluating each value, causing the z-score to approach zero and The anomalies will practically never appearFurthermore, he introduced a compilation error in TypeScript and repeated the same division-by-zero problem as Claude. Worse still, the code wouldn't even compile and wasn't properly tied to the system.

The conclusion of this first round is quite clear: The two Codexes (GPT-5 and GPT-5.1) were the only ones that delivered functional, integrated, and reasonably robust codeGPT-5.1 matched the cost of Claude (about $0,39 in this test), but took less time and had a cleaner architecture.

Test 2 Results: Distributed Alert Deduplication

The second challenge posed a problem of distributed coordination Classic: multiple processors could detect the same anomaly almost simultaneously. It was necessary to prevent duplicate alerts from being triggered when detected within a 5-second window, all while tolerating some clock desynchronization and potential process crashes.

Claude shone once again in the design aspect. He proposed a architecture on three levels: L1 cache, advisory locks on the database as L2, and unique constraints as L3. It used the NOW() from the database to avoid relying on processor clocks, it handled lock release well in case of connection loss and came with almost 500 lines of tests covering conflict, clock skew, and failure scenarios. However, just like in the first test, Nothing was plugged into the actual processor, and some implementation details (such as overly thick lock keys or the time window applied to all active alerts) reduced practical usefulness.

In parallel, GPT-5 Codex He opted for a solution based on a deduplication table with reservations and expiration, coordinated through transactions and FOR UPDATE. The code it was directly integrated into processAlertIt used server time and handled collisions reasonably well, although there was a small race in the clause ON CONFLICT which, under extreme conditions, could allow two processors to pass the same check before committing. It wasn't perfect, but it was very close to something you could deploy with a minor tweak.

The move of GPT-5.1 Codex It was even more minimalist and effective: instead of extra boards, it relied on PostgreSQL consulting locks with a function acquireAdvisoryLock that generated keys using SHA-256 on the pair service:alertTypeUnder that lock, it checked if there were any recent active alerts within the 5-second window and, if not, inserted the new one. If a similar alert already existed, it updated the severity if the new one was higher. All of this with consistent use of server timestamps to manage skew and properly cleaned blocks finallyThe result: simpler logic, without auxiliary tables and without the race that GPT-5 dragged on.

In this test, Whom Yes, he managed to integrate his logic into processAlert and use discrete 5-second buckets with atomic upserts and retries with backoff. The idea itself wasn't bad, but the implementation again failed in key details: when two simultaneous inserts had the same createdAt, the calculation of the flag isDuplicate It was being reversed and the alerts were being flagged incorrectly; furthermore, the bucket recalculation on backoff wasn't even being applied in the query, so They kept trying again on the same conflictIn short, good intuition, poor execution.

Again, in this second round, those who produced the dropdown code were GPT-5 and GPT-5.1 Codex, with a clear advantage for GPT-5.1 in cleanliness and absence of race conditions, all at a cost of about $0,37 compared to $0,60 for GPT-5.

Costs: Why Codex ends up being cheaper than Claude

If you only look at the price per million tokens, you might think that Claude Sonnet 4.5 and GPT-5.1 are in the same league. However, when you delve into the finer numbers of these benchmarks, you see that Codex gives more for lessIn the two combined tests, the costs were approximately as follows:

• Claude: around $1,68 in total.
• GPT-5 Codex: about $0,95 (43% cheaper than Claude).
• GPT-5.1 Codex: approximately $0,76 (around 55% less than Claude).
• kimi: An estimated $0,51, but with much uncertainty due to the lack of a cost breakdown.

The key is that Claude charges more per exit token ($15/M vs. $10/M for GPT-5.1) and, moreover, tends to generate a lot of additional text due to its "think out loud" style and thorough documentation. On the other hand, Codex benefits from context caching in its CLI, reusing large volumes of input tokens without charging them back in full. Add to that the fact that GPT-5.1 was more efficient in terms of the number of tokens used than GPT-5, and the result is a wizard that Not only does it generate more usable code, but it also saves you money..

In the world of fixed-price plans like "20 euros a month", this translates into something very tangible: With Codex you can work many more hours of code before hitting the limit.In contrast, with Claude's plans it's quite common for advanced users to reach the limit even on the most expensive subscriptions, while with Codex Pro it's rare for someone to exceed it except with extreme use.

What GPT-5.1-Codex-Max offers: agents who work all day

Above GPT-5.1 Codex there is a variant specifically designed for very long and detailed works on a codeGPT-5.1-Codex-Max. This model is not geared towards "generic chat", but rather to function as an agent engine within the Codex ecosystem and the OpenAI Codex CLIReading huge repositories, modifying many files, running test suites, and staying the course for hours are part of its DNA.

The key difference is the compactionInstead of relying solely on a gigantic context window, the model is able to go summarizing and condensing It preserves older parts of the session while retaining the details that matter. It's like "zipping" the steps you've already taken to make room for new commands, without forgetting important decisions. Thanks to this, you can work on huge monorepos, interact with multiple services simultaneously, and still remember design choices made hours earlier.

Another interesting point is the levels of reasoningThe "Medium" mode is suitable for everyday tasks (normal tickets, small features, modest refactors) with good latency. The "xHigh" mode gives the model more internal computation time and longer thought processes, sacrificing speed for greater reliability in complex problems: massive refactors, legacy pipelines riddled with pitfalls, difficult-to-reproduce races, etc. For those tasks that would typically consume an entire afternoon for a senior developer, this mode is a worthwhile investment.

In agent-specific benchmarks, GPT-5.1-Codex-Max shows a marked improvement over the standard GPT-5.1 Codex: More tasks completed in SWE-bench Verified and Lancer, better performance in Terminal Bench And, above all, a greater ability to maintain composure during long sessions without getting sidetracked. For many teams, this difference means that an agent can handle an end-to-end ticket instead of just generating one-off patches.

Security, sandboxing, and responsible use of the model

When you give an agent access to your terminal and your repository, it's normal for all your security alarms to go off. Codex and GPT-5.1-Codex-Max are designed to always work within a sandboxIn the cloud, the agent runs in a container with the network disabled by default, and outbound traffic is only allowed if you explicitly enable it. On-premises, it relies on macOS, Linux, or Windows sandboxing mechanisms (or WSL) to limit which files it can access.

There are two rules that are repeated across all Codex surfaces: The network won't open unless you say so.And the agent cannot edit files outside the configured workspace. This, combined with specific training to avoid destructive commands, makes it much more likely that the model will prudently clean up a directory than delete half a project by misinterpreting a phrase like "clean this up."

Regarding attacks from prompt injection (malicious texts that attempt to trick the AI ​​into ignoring its rules and leaking secrets, for example), Codex training insists on treating all external text as untrustworthy, supported by best practices of automated testing for AI modelsIn practice, this translates into rejections of data leak requests, refusal to upload private code to external websites, and a strong preference for following system and developer instructions over anything found in documentation or on web pages.

GPT-5.1 Codex versus Claude and other models in everyday use

Once the specific benchmarks and capabilities of Codex-Max have been examined, the overall picture becomes quite clear: Each model has its ideal niche.And the sensible thing is not to stick with just one for everything, but to know when to use each tool.

GPT-5.1 Codex (and its Max variant) fit especially well when you need Integrated code, with attention to edges and little room for errorIn both observability tests, it was, along with GPT-5, the only implementation that could be deployed in production without rewriting half the file. Furthermore, the cost per task was the lowest of all, with efficiency improvements over GPT-5 and a price-performance ratio that was hard to beat.

Claude Sonnet 4.5 / Claude Code They shine when what you want is architectural design, in-depth documentation and explanationsThink architecture reviews, extensive technical documents, migration guides… Their solutions tend to be very well reasoned and well explained, with layers of defense and trade-off analyses that are a pleasure to read. The price to pay: prototypes that then need to be wired manually, more critical bugs than initially apparent, and a significantly higher cost per token.

Kimi K2 Thinking provides a lot of creativity and alternative approachesIn his experiments, he tested some interesting ideas, such as temporary bucket windows for deduplication and combinations of MAD and EMA for anomaly detection. Furthermore, his CLI is inexpensive, though somewhat underdeveloped. The problem is that it often falters in the core logic details: the order in which statistics are updated, division by zero, inverted flags, etc. It's great for inspiration, but you need to dedicate significant time to refining and testing its output.

Finally, the general GPT-5.1 models (Instant and Thinking) and models such as Gemini or Llama serve as a basis for mixed tasks (documentation, data analysis, user interaction), but when the task is purely code and agent-based, the Codex package currently offers a combination of depth, price and tooling quite difficult to match.

Looking at everything together—the two observability benchmarks, the extended use in IDEs like VS Code and Cursor, the compaction of Codex-Max, the reasoning modes, and the cost differences—the overall impression is quite clear: In the field of "AI that actually programs and delivers decent pull requests", GPT-5.1 Codex has earned the role of a leading toolClaude Code remains an excellent companion for architectural thinking and producing superb documentation, and Kimi or similar models provide spark and alternatives, but when it comes to producing code that compiles, integrates, and doesn't crash on the first try, the Codex side is usually the one that ends up pushing master.

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