• GPT-4.1 is a significant improvement over GPT-4.0 in terms of coding capabilities, achieving 55% on the SU verified Python coding benchmark, outperforming even larger reasoning models like GPT-3.5.
• The new models in the GPT-4.1 family (GPT-4.1, GPT-4.1 Mini, and GPT-4.1 Nano) demonstrate better intelligence at lower latency and cost compared to GPT-4.0.
• GPT-4.1 shows significant improvements in instruction following, with better performance on tasks like format following, negative instructions, ordered instructions, content requirements, and ranking over-confidence.
• The 1 million token context window of GPT-4.1 is a major advantage, but the model's ability to effectively retrieve and understand information from this long context is crucial. OpenAI has introduced new benchmarks like "multi-round co-reference" to test this capability.
• On the multi-round co-reference benchmark, GPT-4.1 outperforms previous GPT models and even some reasoning models when retrieving multiple pieces of information from the long context.
• However, the reasoning models like GPT-3.5 still maintain an advantage when the number of "needles in the haystack" increases, suggesting that long-context retrieval is an area that still needs improvement.
• The pricing of the GPT-4.1 models is significantly lower than GPT-4.0, making them a more viable option for developers, though still more expensive than some alternatives like Gemini 2.5 Pro.

OpenAI's new GPT-4.1 models, including the GPT-4.1, GPT-4.1 Mini, and GPT-4.1 Nano, claim significant improvements over the previous GPT-4.0 models. The new models boast a larger 1 million token context window, reduced latency, and lower costs. However, the knowledge cutoff date is June 2024, which may be a limitation for some coding-related tasks.

When comparing the GPT-4.1 family to other providers, the benchmarks show mixed results. On the Sweetbench verified Python coding benchmark, GPT-4.1 outperforms GPT-4.0 but falls just behind the Amazon Q developer agent, which was specifically designed for coding tasks. On the more challenging ADAS polyglot benchmark, GPT-4.1 performs well compared to GPT-4.0 but lags behind OpenAI's own reasoning models like GPT-3.5.

In terms of instruction following, GPT-4.1 demonstrates significant improvements over GPT-4.0, particularly in areas like format following, negative instructions, ordered instructions, and content requirements. This is crucial for building reliable coding models that can follow instructions precisely.

The new multi-round co-reference benchmark, which tests the model's ability to retrieve and understand multiple pieces of information from long-form text, reveals that GPT-4.1 outperforms GPT-4.0 and even some reasoning models when dealing with a small number of "needles in the haystack." However, as the number of needles increases, the reasoning models start to outperform GPT-4.1.

In terms of pricing, GPT-4.1 is significantly more affordable than GPT-4.0, with a 25% reduction in cost. This makes it a more viable option for developers, especially when compared to other high-performing models like Gemini 2.5 Pro.

Overall, the GPT-4.1 family appears to be a substantial improvement over the previous GPT-4.0 models, particularly in the areas of coding, instruction following, and long-form text retrieval. However, it still has room for improvement when compared to some of the top-performing models from other providers, especially in more complex reasoning tasks.

According to the benchmarks presented, the GPT-4.1 family of models demonstrates significant improvements in coding performance compared to the previous GPT-4.0 version. On the Sweetbench verified benchmark, GPT-4.1 achieves a 55% score, which is better than GPT-4.0. However, it still lags behind the Amazon Q developer agent, a model specifically designed for coding tasks.

When tested on the more challenging ADAS polyglot benchmark, which covers a wider range of programming languages, GPT-4.1 again outperforms GPT-4.0 but falls behind the OpenAI reasoning models, such as GPT-3.5 and GPT-4.5. The performance on the "whole" and "diff" versions of the polyglot coding benchmark suggests that GPT-4.1 may not be the top choice for coding tasks, as models like Gemini 2.5 Pro and potentially DeepSQ W3 and DeepSQ R1 may provide similar or better performance at a relatively comparable cost.

However, OpenAI has made significant improvements in the front-end development capabilities of GPT-4.1, as evidenced by the visually superior front-end outputs compared to GPT-4.0 when given the same prompts.

OpenAI has highlighted significant improvements in GPT-4.1's instruction following capabilities across a variety of tasks. The model demonstrates more reliable adherence to instructions, including:

• Format Following: GPT-4.1 follows formatting instructions more consistently.
• Negative Instructions: The model better understands and follows instructions that involve negation or avoidance of certain actions.
• Ordered Instructions: GPT-4.1 maintains the correct order of steps in multi-part instructions.
• Content Requirements: The model is better at ensuring the output meets all specified content requirements.
• Ranking Over Confidence: GPT-4.1 is more adept at ranking its own responses and avoiding overconfident outputs that do not fully meet the instructions.

These improvements are critical for building effective coding models, as developers need the model to follow instructions precisely rather than generating creative but divergent solutions.

Additionally, GPT-4.1 has shown significant enhancements in multi-turn instruction following and maintaining coherence throughout longer conversations. This is an important capability for many developer-facing applications, where the model needs to remember and build upon previous context provided by the user.

Overall, the instruction following capabilities of GPT-4.1 appear to be a major strength of this new model, making it a compelling option for developers who require reliable, context-aware execution of their instructions.

One of the key improvements in GPT-4.1 is its ability to effectively retrieve and understand multiple pieces of information from large contexts. The model's 1 million token context window allows it to maintain and leverage a vast amount of relevant information.

OpenAI has introduced a new benchmark called "multi-round co-reference" to specifically test the model's long-context retrieval capabilities. This benchmark evaluates the model's ability to find and distinguish between multiple "needles" (specific pieces of information) hidden within longer passages of text.

The evaluation involves multi-turn synthetic conversations where the user requests information on a topic, and the model must retrieve the correct response corresponding to the specific instance of the request, even when there are multiple identical requests scattered throughout the context.

In this benchmark, GPT-4.1 demonstrates significant improvements over its predecessor, GPT-4.0, as well as other reasoning-focused models. The model's performance remains strong even as the number of "needles" in the context is increased, outperforming the reasoning models in these more challenging scenarios.

However, the report notes that after a certain context size (around 128,000 tokens), the performance of even the long-context models begins to plateau. This suggests that there are still opportunities for further advancements in long-range retrieval and understanding.

Overall, the long-context retrieval capabilities of GPT-4.1 make it a compelling option for developers building systems that require the extraction and comprehension of multiple pieces of information from large, complex datasets.

OpenAI's new GPT-4.1 models have demonstrated impressive performance on multimodal reasoning benchmarks, matching or exceeding the capabilities of top models in tasks involving both text and visual inputs.

On the MMU (Multimodal Understanding) benchmark, GPT-4.1 achieved a score of 75%, placing it on par with the performance of models like Llama 4 Behemoth. This suggests that the new GPT-4.1 models have made significant strides in their ability to reason across textual and visual modalities.

Furthermore, on the Video MME (Multimodal Embedding) benchmark, which evaluates the model's understanding of long-form video contexts, GPT-4.1 achieved a score of 72%. This performance is comparable to that of the Intern Vision Language Model 2.5 from the Shanghai lab, a 72 billion parameter model.

These results indicate that the GPT-4.1 models have made substantial improvements in their multimodal reasoning capabilities, allowing them to effectively process and reason about information from both textual and visual sources. This is a crucial capability for many real-world applications, such as question-answering, content generation, and decision-making tasks that require the integration of diverse data sources.

The most appealing aspect of the GPT-4.1 release for developers, aside from the performance improvements, is the pricing. Compared to the previous GPT-4.0 model, the pricing for GPT-4.1 is significantly lower, around 25-26% less expensive.

This makes GPT-4.1 a much more viable option for developers, especially when considering the model's enhanced capabilities in areas like coding and instruction following tasks. The reduced cost, combined with the performance gains, make GPT-4.1 a compelling replacement for the older GPT-4.0 model.

However, when compared to other providers like Gemini, the pricing for GPT-4.1 may still be relatively high. For example, Gemini 2.5 Pro could be a better option for use cases with less than 200,000 input tokens, as its pricing is more favorable. But for workloads with more than 200,000 tokens, GPT-4.1 may be the more cost-effective choice.

Additionally, upcoming options like Gemini 2.0 Flash or the new Gemini 2.5 Flash could provide even more competitive pricing, potentially making them attractive alternatives to GPT-4.1 for certain use cases. Overall, the significant cost reduction in GPT-4.1 compared to its predecessor is a notable advantage, but developers should still evaluate their specific needs and compare pricing across different providers to find the most suitable solution.

The release of GPT-4.1 by OpenAI represents a significant improvement over the previous GPT-4.0 model, particularly in areas such as coding instruction following and front-end development. The new models, including the GPT-4.1, GPT-4.1 Mini, and GPT-4.1 Nano, offer enhanced capabilities and reduced latency and cost compared to their predecessor.

However, when compared to other specialized models, GPT-4.1 still has room for growth. While it outperforms GPT-4.0 on various benchmarks, it lags behind some of the reasoning-focused models, such as the OpenAI 01 series, in tasks that require retrieving and understanding multiple pieces of information from long-form contexts.

The introduction of new benchmarks, such as the multi-round co-reference and graph tasks, highlights OpenAI's efforts to address the limitations of simpler "needle in the haystack" tests. These new benchmarks suggest that GPT-4.1 performs well in retrieving specific information from long contexts, but may still be outpaced by more specialized models in complex, multi-step retrieval and reasoning tasks.

Ultimately, the success of GPT-4.1 will depend on its real-world performance and adoption by developers. The model's improved coding and instruction-following capabilities, combined with its reduced cost and latency, make it a compelling option for many use cases. However, for applications that require advanced reasoning and long-form context understanding, users may still need to consider alternative models that are more tailored to those specific requirements.