GPT-4.1 Benchmarks in 2026: Should You Still Use It, or Move to GPT-5?

TL;DR: GPT-4.1 in May 2026

GPT-4.1 benchmark recap (April 2025 launch numbers, still valid)

These are the numbers OpenAI published at launch. They have not been retracted, and the coding lift was widely reported at launch (Reuters).

Coding: 54.6% on SWE-bench Verified

• SWE-bench Verified: 54.6%, up from GPT-4o’s 33.2%.
• Code-diff accuracy: 52.9%, up from GPT-4o’s 18.3% (more than doubled).
• Unnecessary edits: dropped from 9% on GPT-4o to 2%.
• Reuters independent take: 21% improvement in real-world coding performance over GPT-4o.

For context: GPT-5 now hits 74.9% on SWE-bench Verified per the GPT-5 release blog. Anthropic’s most recent Claude releases also report strong SWE-bench Verified results in the 70s and above. Check the current Anthropic release notes for the exact number before depending on it. The frontier moved.

Instruction following

• MultiChallenge (multi-turn context tracking): 38.3%, up from GPT-4o’s 27.8%.
• IFEval (unambiguous instruction compliance): 87.4%, up from GPT-4o’s 81.0%.

Long-context comprehension

• Needle-in-haystack at 1M tokens: 100% accuracy.
• Video-MME (30-60 minute videos, no subtitles): 72%, up 6.7 points from GPT-4o.

Variant pricing (current as of May 2026)

Prices verified against openai.com/api/pricing on the publish date of this update.

GPT-4.1 vs GPT-5 vs Claude Opus 4.7 vs Gemini 3 Pro

The 2025 comparison table is outdated. Here is the May 2026 comparison.

Numbers from each vendor’s own benchmarks as of May 2026. Always run your own eval before standardising on one.

How to access GPT-4.1

GPT-4.1 is API-only. Sign up at platform.openai.com, generate a key, and reference the model string in any chat completions or responses call:

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4.1",  # or "gpt-4.1-mini", "gpt-4.1-nano"
    messages=[{"role": "user", "content": "Summarise this changelog in 3 bullets."}],
)

The OpenAI Playground still hosts all three variants at platform.openai.com/playground.

The migration question: stay on 4.1 or move to GPT-5?

A decision matrix that has held up across customer rollouts in the year since GPT-5 shipped:

Before flipping production traffic, run shadow mode: send the same prompts to both models, log to traceAI spans, score with fi.evals.evaluate. Require at least 1,000 paired evaluations.

How Future AGI helps you evaluate and monitor GPT-4.1 in production

Three pieces of the Future AGI platform matter for evaluating GPT-4.1 (or any model) in production:

• Evaluate runs 50 plus built-in metrics in parallel (groundedness, exact match, format compliance, toxicity, custom LLM-as-judge). Source under Apache 2.0 at github.com/future-agi/ai-evaluation.
• traceAI captures every prompt, response, latency, and token count per span. Apache 2.0 OpenTelemetry instrumentation at github.com/future-agi/traceAI.
• Agent Command Center routes GPT-4.1 and GPT-5 traffic side by side with BYOK pricing across major frontier and open-source providers. Useful for A/B testing model swaps with no code change.

import os
from fi.evals import evaluate, Evaluator

os.environ["FI_API_KEY"] = "..."
os.environ["FI_SECRET_KEY"] = "..."

# Compare two model outputs on the same input + reference context.
gpt41_out = "Output from gpt-4.1 for the same prompt..."
gpt5_out = "Output from gpt-5 for the same prompt..."

score_41 = evaluate(
    evaluator=Evaluator.GROUNDEDNESS,
    input="Question about retrieved doc.",
    output=gpt41_out,
    context=["Retrieved chunk 1", "Retrieved chunk 2"],
)
score_5 = evaluate(
    evaluator=Evaluator.GROUNDEDNESS,
    input="Question about retrieved doc.",
    output=gpt5_out,
    context=["Retrieved chunk 1", "Retrieved chunk 2"],
)
print(score_41, score_5)

Loop the same call over your labelled prompts and aggregate per model. Set FI_API_KEY and FI_SECRET_KEY and the runs log to the dashboard. Free tier covers 50 GB tracing and 2,000 AI credits a month: futureagi.com/pricing.

Why GPT-4.1 is still a sensible choice for coding, long-context, and cost-sensitive teams in 2026

GPT-5 takes the coding crown. GPT-4.1 keeps the cost-efficiency crown for short-prompt high-volume work, plus a permanent 1M token context that not every newer model matches. The right answer is not always “use the latest model”. The right answer is “match the model to the workload, prove it with an eval, and revisit when prices shift again”.

If you ship inference at scale, set up a recurring eval that scores your top three candidate models against last week’s production prompts. The cheapest model that passes your quality bar wins. In some workloads that is still GPT-4.1.

Sources

• OpenAI GPT-4.1 launch post (April 14, 2025)
• OpenAI GPT-5 launch post (August 2025)
• OpenAI API pricing
• OpenAI models reference
• Anthropic news and product announcements
• Reuters coverage of GPT-4.1 launch
• Artificial Analysis GPT-4.1 page
• Future AGI evaluation library, Apache 2.0
• traceAI, Apache 2.0 OpenTelemetry instrumentation

For deeper context, see LLM benchmarking compared and what is LLM evaluation in 2026.