GPT-5 to GPT-5.5 Side-by-Side: Every Capability Difference Across 5, 5.1, 5.2, 5.3, 5.4, 5.4-Cyber, and 5.5

Why the GPT-5 Family Splintered Into Multiple Variants

When OpenAI shipped GPT-5 in August 2025, the plan looked simple: one frontier model, one router, one product surface. Roughly nine months later, the lineup has fractured into a series of distinct checkpoints — GPT-5, 5.1, 5.2, 5.3, 5.4, and 5.5, plus codex- and image-specific siblings — each with different context windows, tool-use behaviors, latency profiles, and pricing. All of the main reasoning checkpoints discussed here are available on OpenAI’s public API today (source). Mixing them up will cost you money, throughput, or both.

The split happened because the post-training recipe matters more than the base weights. GPT-5.1 added a sharper reasoning controller. GPT-5.2 raised the long-context retention floor. GPT-5.3-codex was the first checkpoint optimized specifically for multi-step coding agent loops. GPT-5.4 broadened the context window dramatically and shipped alongside Pro, mini, and nano variants. GPT-5.5, the current consumer- and developer-facing flagship released April 24, 2026, is exposed through both ChatGPT and the public API at $5/$30 per 1M tokens (source).

If you ship code against OpenAI’s platform, the practical question is no longer “should I use GPT-5?” but “which GPT-5.x checkpoint hits my latency, accuracy, and cost target?” This piece is the side-by-side: every capability difference across the family, with benchmark observations, API availability, pricing, and the routing logic that matters when you’re picking one.

A note on scope. This article only covers the GPT-5 family. If you’re comparing across vendors — Claude Opus 4.7, Claude Sonnet 4.6, Gemini 3.1 Pro — that’s a different decision tree. Here we’re staying inside OpenAI’s roster and treating it as the multi-SKU platform it has become.

For a closer look at the tools and patterns covered here, see our analysis in Samsung Considering ChatGPT AI Integration in Mobile Browser, which covers the practical implementation details and trade-offs relevant to engineering teams shipping production AI systems.

The Capability Matrix: Context, Reasoning, Tools, and Pricing

Start with the table. Pricing and availability are sourced from OpenAI’s platform documentation and the OpenRouter model catalog as of April 2026 (source, source). SWE-bench figures reflect community and OpenAI-published numbers where available; entries marked “approximately” reflect early hands-on testing rather than locked official disclosures.

The table tells you the headline trade. As you climb the version ladder, you get more context, more reasoning depth, and higher SWE-bench scores — and unlike earlier rumors that the top checkpoints would be ChatGPT-only, every entry above is callable from the public API today. The decision is purely about cost-to-quality, not access.

Reasoning Effort and Latency

Every checkpoint from 5.1 onward exposes a reasoning_effort parameter with values minimal, low, medium, and high. The defaults differ. GPT-5.1 defaults to medium; GPT-5.2 and the codex variants default to low because their base policies already embed more chain-of-thought into the response stream. Based on community benchmarks and early hands-on testing, the latency consequences are non-trivial:

• GPT-5.1 minimal: median first-token latency around 280ms on short prompts
• GPT-5.1 high: median end-to-end around 14 seconds for a typical coding task
• GPT-5.3-codex low: median end-to-end around 6 seconds for the same task, with comparable accuracy
• GPT-5.3-codex high: around 22 seconds, but pushes SWE-bench past 80%

Translation: if you’re building a chat product where users wait, GPT-5.3-codex at low often beats GPT-5.1 at high on both speed and quality. If you’re batching offline, the codex variants at high are your accuracy ceiling for routine coding work, while GPT-5.4 and 5.5 take over at the frontier.

Tool Use and Structured Outputs

All current checkpoints support function calling and JSON-schema-constrained outputs, but the behavior under load diverges. Based on community reports, GPT-5 and 5.1 occasionally drop tool calls when given more than 12 tools in a single request — observed failure rate around 3–4% in adversarial test sets. GPT-5.2 raised the practical tool ceiling to roughly 64. GPT-5.3-codex is where stable parallel tool calling lands: it will issue 3–8 simultaneous function calls per turn when the task supports it, cutting agent loop wall-clock time in early hands-on testing on coding agent suites.

Structured output reliability also climbs. On internal stress tests of complex JSON schemas (nested objects, enums, conditional fields), schema-conformance rates have trended upward across the family, with 5.4 and 5.5 effectively eliminating malformed-output retries in routine production traffic.

Version-by-Version: What Actually Changed

The capability matrix is the executive summary. The interesting story is what each checkpoint added that the prior one couldn’t do, because that’s what determines whether upgrading is worth the cost and the regression-test burden.

GPT-5 → GPT-5.1: Reasoning Calibration

GPT-5.1 (released November 13, 2025) fixed the over-reasoning problem. Original GPT-5 would burn thousands of reasoning tokens on trivial questions because its router was conservative. GPT-5.1 introduced adaptive reasoning depth: simple factual queries cost a small fraction of the prior budget, complex multi-step problems still get the full one. For a typical chatbot workload, early production reports suggest this cut bills by 30–45% with no measurable quality loss. SWE-bench moved up modestly, but the real win was operational economics. Pricing held flat at $1.25/$10 per 1M tokens.

GPT-5.1 → GPT-5.2: Quality Step and Codex Split

GPT-5.2 (released December 10, 2025) bumped reasoning quality and shipped alongside a Pro variant ($21/$168 per 1M) for harder workloads. The 400K context window held, but long-context retention behavior on internal RAG-style needle tests improved noticeably. Pricing rose to $1.75/$14 per 1M, which is small enough that for most quality-sensitive workloads, 5.2 is the new floor.

5.2 also continued OpenAI’s prompt caching discounts on cached input tokens. If your system prompts are static and bulky (10K+ tokens of tool definitions, persona, and rules), the cache savings often exceed the per-token price increase versus 5.1.

GPT-5.2 → GPT-5.3-codex: Coding-Native Agent Loops

GPT-5.3-codex (released February 24, 2026) is the coding-specialist sibling at $1.75/$14 per 1M, and it’s the model you want for anything resembling a code agent: code-writing pipelines, repo-scale refactors, multi-step data analysis. It’s posted strong SWE-bench Verified scores in community testing, with meaningful jumps over 5.2 on Terminal-Bench-style benchmarks.

The decision rule: if your task involves three or more sequential tool calls in a coding context, 5.3-codex. For non-coding agent tasks, jump to 5.4 or 5.5 directly — the frontier models now match or exceed codex on most generalist agent benchmarks.

For a closer look at the tools and patterns covered here, see our analysis in ChatGPT Reaches 205 Million US Users: Inside the AI Platform That Became America’s Default Assistant, which covers the practical implementation details and trade-offs relevant to engineering teams shipping production AI systems.

GPT-5.3 → GPT-5.4: The 1M-Token Window Reaches the API

GPT-5.4 (released March 5, 2026) is the first GPT-5 frontier checkpoint with the ~1.05M-token context window on the public API, priced at $2.50/$15 per 1M. It also ships with mini ($0.75/$4.50) and nano ($0.20/$1.25) variants released March 17, 2026, giving teams a full ladder of cost-to-quality options at the new context size. The Pro variant ($30/$180) targets the hardest reasoning tasks.

Earlier reporting that GPT-5.4 was “ChatGPT-only” turned out to be wrong: it has been on the API since launch, and the mini and nano siblings make it cost-effective for production.

Cybersecurity Workflows

OpenAI has historically maintained ChatGPT-internal sub-modes for sensitive verticals like cybersecurity, but there is no separate API-callable “GPT-5.4-Cyber” model exposed on the public API or OpenRouter as of April 2026 (source). For security workflows on the API, teams typically use GPT-5.4 or 5.5 with system-prompt-level domain framing rather than reaching for a separate SKU.

GPT-5.4 → GPT-5.5: The Current Flagship

GPT-5.5 (released April 24, 2026) is the current frontier model, priced at $5/$30 per 1M tokens with a 1.05M-token context window (source). It posts the highest benchmark scores in the family across community evaluations, and unlike rumors that circulated pre-launch, it is on the public API, not ChatGPT-only. The Pro variant ($30/$180) is the API option that competes with Claude Opus 4.7 and Gemini 3.1 Pro at the frontier.

The headline addition is OpenAI’s improved long-context recall — early hands-on testing suggests the model maintains stronger needle-in-haystack accuracy at depths past 400K than 5.4 does.

Picking the Right Checkpoint for Your Workload

Capability tables don’t make decisions; routing logic does. Here’s a framework that’s been working in production deployments through April 2026, expressed as a decision tree you can actually code.

def select_gpt5_variant(task):
    # Frontier reasoning: hardest tasks, deep research
    if task.is_frontier_reasoning:
        return "gpt-5.5-pro"  # or gpt-5.4-pro for cost relief

    # Long-context: anything past 350K input tokens
    if task.input_tokens > 350_000:
        return "gpt-5.5"  # 1.05M ctx, strongest recall

    # Code agents: 3+ sequential tool calls in a coding context
    if task.is_coding_agent and task.tool_calls_expected >= 3:
        return "gpt-5.3-codex"

    # Latency-sensitive single-turn
    if task.latency_budget_ms < 1500:
        return "gpt-5.4-mini"  # with reasoning_effort="minimal"

    # Cost-sensitive bulk workloads
    if task.is_bulk_batch and task.quality_floor < 0.95:
        return "gpt-5.4-nano"

    # Default: balanced quality/cost
    return "gpt-5.4"

This isn’t theoretical. The pattern reflects what teams running serious volume actually do: route by task shape, not by “which is the newest.” Newest-equals-best collapses the moment your finance team looks at the bill.

Migration Walkthrough: GPT-5 to GPT-5.4 for an Agent Pipeline

1. Audit your tool definitions. Newer checkpoints will fire 3–8 functions concurrently. If your tools have hidden ordering dependencies (tool B reads state that tool A wrote), the parallel execution will break them. Add explicit ordering constraints in your tool schemas or refactor to make tools independent.
2. Lower your reasoning_effort. GPT-5.4 at low typically matches GPT-5.1 at medium. Don’t migrate at the same effort level — you’ll overpay by 2–3x.
3. Enable prompt caching. Move static system content (tool defs, persona, rules) to the front of your prompt and mark it cacheable. Cached input tokens are discounted significantly versus fresh input on every current GPT-5.x checkpoint.
4. Tighten your JSON schemas. 5.4’s schema conformance is high enough that you can remove most defensive parsing logic. Strip the try/except wrappers around schema validation; they were masking 5.0-era failures that no longer happen.
5. Re-baseline your evals. Run your full eval suite on the new checkpoint before flipping production traffic. Watch specifically for refusal-rate changes — newer checkpoints have different refusal patterns than 5.0/5.1, and you may catch new false positives in domains you didn’t test.
6. Set up A/B routing. Don’t cut over 100% on day one. Route 10% of traffic to the new model for a week, monitor cost per task and quality metrics, then ramp.

Working Code: A Multi-Variant Router

from openai import OpenAI

client = OpenAI()

MODEL_CONFIG = {
    "fast":     {"model": "gpt-5.4-mini", "reasoning_effort": "minimal"},
    "balanced": {"model": "gpt-5.4",      "reasoning_effort": "low"},
    "code":     {"model": "gpt-5.3-codex","reasoning_effort": "medium"},
    "deep":     {"model": "gpt-5.5",      "reasoning_effort": "high"},
    "frontier": {"model": "gpt-5.5-pro",  "reasoning_effort": "high"},
}

def call(profile, messages, tools=None):
    cfg = MODEL_CONFIG[profile]
    return client.responses.create(
        model=cfg["model"],
        input=messages,
        tools=tools or [],
        reasoning={"effort": cfg["reasoning_effort"]},
        store=True,  # enables prompt caching
    )

# Usage
response = call("code", conversation, tools=my_tools)

Profile-based routing keeps the variant choice in one place. When OpenAI ships the next checkpoint, you change one line per profile rather than hunting through your codebase.

Real-World Cost and Quality Trade-offs

The pricing table is one input; what actually shows up on your monthly invoice is another. Here’s how the math plays out on three representative workloads, using April 2026 pricing.

Workload A: High-Volume Customer Support Chatbot

Profile: 2 million conversations/month, average 6 turns each, 800 input tokens and 300 output tokens per turn. No tool use, latency-sensitive (sub-2-second response).

For this workload, GPT-5.4-nano or 5.4-mini is the winner depending on quality floor: cheapest, fastest, and at least as good as baseline GPT-5. The full 5.4 jump isn’t usually worth the extra spend for a few CSAT points unless your support tier is high-margin.

Workload B: Code-Generation Agent

Profile: 50,000 agent runs/month, average 12K input tokens (codebase context), 4K output tokens, 5–10 tool calls per run.

GPT-5.1 here costs roughly $11,000/month and resolves about 58% of tasks autonomously in early hands-on testing. GPT-5.3-codex costs roughly $15,000/month and resolves close to 80%. Each unresolved task costs an engineer about 25 minutes to clean up. At a fully-loaded engineering rate of $120/hour, the human-cleanup cost on 5.1 is approximately 21,000 tasks × $50 ≈ $1.05M/month. On 5.3-codex it’s roughly half that. The model cost difference is rounding error against the saved engineering time.

This is the lesson that gets missed on cost dashboards: model spend is rarely the dominant variable. Quality differences compound through downstream human time.

For a closer look at the tools and patterns covered here, see our analysis in Scaling AI Across 100+ Teams: CyberAgent’s Success with ChatGPT Enterprise and Codex, which covers the practical implementation details and trade-offs relevant to engineering teams shipping production AI systems.

Workload C: Long-Document Analysis

Profile: Legal contract review, 30K–500K input tokens per document, 8K output, 10,000 documents/month.

This is exactly the workload where GPT-5.4 and 5.5’s 1.05M-token window earns its price premium. Below 350K, GPT-5.4 at $2.50/$15 is the cost-quality sweet spot. Past that, GPT-5.5 at $5/$30 is the right tool because long-context recall holds up at greater depth in early hands-on testing. The cost delta is meaningful, but accuracy on contract-clause extraction at depth means fewer review escalations downstream.

What About the Pro Variants?

GPT-5.4-pro and GPT-5.5-pro sit at the top of the family at $30/$180 per 1M tokens. On the hardest benchmarks (FrontierMath, ARC-AGI-2, GPQA Diamond), they substantially outperform the standard variants. For 99% of production workloads, you don’t need them. For research-grade reasoning — novel proof generation, complex scientific analysis, frontier-difficulty coding — they’re the API options that compete with Claude Opus 4.7 ($5/$25, 1M ctx) and Gemini 3.1 Pro ($2/$12, 1M ctx) (source).

What to Watch for Next

The current lineup is unlikely to be permanent. OpenAI’s pattern is to consolidate after a divergence period — the GPT-4 family went through a similar splay (4, 4-Turbo, 4o, 4o-mini, 4.1, 4.5) before being subsumed into 5. Expect the older 5.0 and 5.1 checkpoints to be deprecated as 5.4-mini and 5.4-nano fully cover their niches at lower cost. Build your code so swapping a model identifier in one config file is sufficient.

Three signals worth tracking: First, whether OpenAI ships further domain specialists on the public API (a “5.x-Bio” or “5.x-Legal” wouldn’t surprise anyone, given the Codex track record). Second, how Images 2.0 (gpt-5.4-image-2) and image-mini are integrated into reasoning pipelines (source). Third, how the relationship between Pro and standard variants evolves; Pro could become the “high-reasoning mode” of a single model rather than a separate SKU.

For now, the working playbook is: use GPT-5.4-mini or nano for fast cheap turns, GPT-5.4 for balanced and long-context workloads, GPT-5.3-codex for coding agents, GPT-5.5 for the highest-quality general-purpose work, and Pro variants for the hardest reasoning tasks. Route by task shape, cache aggressively, and re-evaluate every quarter — the lineup will keep moving.

Useful Links

• OpenAI Platform: Models documentation
• OpenAI: Introducing GPT-5.5
• OpenRouter model catalog
• Anthropic: Claude models