[Complete Guide] OpenAI “GPT-5” Models, Fully Explained: Differences & How to Choose — gpt-5 / mini / nano / chat-latest / Thinking / Pro

Quick TL;DR up front
– Latest flagship released August 7, 2025. In ChatGPT, it has evolved into an integrated system that auto-switches between “fast replies” and “deep reasoning.”
– Model lineup at a glance (roles differ slightly between the ChatGPT experience and the API):
　• ChatGPT: GPT-5 (standard) / GPT-5 Thinking (deeper reasoning) / GPT-5 Pro (thinks even longer/deeper)
　• API: gpt-5 (reasoning model) / gpt-5-mini / gpt-5-nano (lightweight) / gpt-5-chat-latest (roughly the “non-reasoning” side used in ChatGPT)
– Context window up to 400K tokens, max 128K-token output (shared across sizes).
– Reference pricing (API): gpt-5 $1.25 (input) / $10.00 (output); gpt-5-mini $0.25 / $2.00; gpt-5-nano $0.05 / $0.40 (all per 1M tokens).
– Strengths: real-world coding, instruction-following & tool use (agent-like tasks), multimodal understanding, and health-related queries at SOTA levels.
– New parameters: reasoning_effort (how deeply to think) and verbosity (brief ↔ detailed). Custom tools (tool calls in plain text, not JSON) are also added.
– User-facing wins: Large drop in hallucinations and reduced sycophancy. From everyday writing/summarization/design reviews to serious repo edits, accuracy and completion power in real work are up.
– Rollout: ChatGPT defaults to GPT-5. Message caps and Thinking quotas by plan are clarified; automatic smart switching keeps usage simple.

1. What is GPT-5? — An integrated system that auto-chooses “speed” vs. “deep thinking”

GPT-5 unifies the experience of a “fast response model” and a “deliberate reasoning model.” In ChatGPT, a router instantly selects the optimal mode based on question type/complexity, needed tools, and even explicit intent like “think it through.” Most questions go to a fast model; hard ones go to GPT-5 Thinking; and when usage hits limits, it fails over to a mini variant automatically. With this standardized “brain switching,” users are freed from manual model selection. Release date: August 7, 2025, and this new experience is the default in ChatGPT.

This refresh notably improves real-world answer accuracy, faithful instruction-following, and lower sycophancy—improvements that matter for usefulness over “sounding good.” From daily writing to code fixes, design alignment, and medical-information comprehension, GPT-5 feels like a more reliable partner for real tasks.

2. The lineup: ChatGPT’s UX design vs. API offerings

2-1. ChatGPT (product) design

ChatGPT uses a router to choose between GPT-5 (fast/standard) and GPT-5 Thinking (deep reasoning). GPT-5 Pro is a variant that thinks longer and deeper, suited for toughest problems and meticulous analysis. Thinking/Pro intelligently use parallel trial computations, enabling broad exploration and fine-grained verification before answering. The UI shows a subtle indicator while it reasons; you can switch to “answer now” if needed.

Voice mode continues to use GPT-4o (for continuity of the voice experience). Old threads migrate to the closest GPT-5 family based on the model previously used (e.g., o3 → GPT-5 Thinking, o3-Pro → GPT-5 Pro).

2-2. API (developer) lineup

Developers directly use the reasoning-capable, developer-facing GPT-5 via three sizes:

• gpt-5: Full capability; core for reasoning and tool use.
• gpt-5-mini: Cost/latency-optimized, practical for many workloads.
• gpt-5-nano: Even lighter; ideal for high-speed batch and large-volume tasks.

Additionally, the API exposes gpt-5-chat-latest, corresponding to ChatGPT’s non-reasoning side. You can control how hard it thinks via reasoning_effort, and adjust brevity vs. detail via verbosity. Custom tools allow plain-text tool calls (no JSON). Sequential/parallel tool calls and search over long contexts are stronger than before.

2-3. Internal labels in the system card (FYI)

The system card references internal labels that underpin the ChatGPT experience—gpt-5-main (fast) / gpt-5-thinking (reasoning) with mini/nano variants, plus gpt-5-thinking-pro. It also shows a lineage (e.g., 4o → gpt-5-main, o3 → gpt-5-thinking) and notes that a single-model unification is envisioned in the future (these are internal descriptors, not official API names).

3. Specs & pricing: 400K context / 128K output, three sizes to fit the job

Max context 400K tokens; max output 128K tokens—shared across gpt-5, gpt-5-mini, and gpt-5-nano. Prices (per 1M tokens): gpt-5: $1.25 (in) / $10.00 (out); gpt-5-mini: $0.25 / $2.00; gpt-5-nano: $0.05 / $0.40. gpt-5-chat-latest pricing follows gpt-5. The offering balances ample memory for large research/long audit trails with tiered price points for practical use.

Mini tip
Even with a 400K context, design in stages—“give the gist first” → “progressively fetch details.” Start with headlines & key evidence, then have the model auto-pull needed references. This stabilizes both cost and quality.

4. Where it shines — Updates that help on the job

4-1. Coding (SWE-bench Verified 74.9%, Aider polyglot 88%)

GPT-5 is strong in real-project coding, posting 74.9% on SWE-bench Verified and 88% on Aider polyglot. It improved at self-running the loop from planning/decomposition → implementation → validation → fixes. For frontend UI, aesthetics and structure co-exist; an internal eval reports 7 in 10 cases preferred over o3. It’s good at understanding large repos and applying local fixes, and it stays on track in long agent tasks—useful in practice.

4-2. Instruction-following & tool use (τ²-bench 96.7%)

Standardized sequential/parallel tool calls, error recovery, and mid-progress preambles make its work style presentable by default. On a customer-support flavored tool-use benchmark (τ²-bench), it hit 96.7%—strong at multistep, real execution. Custom tools accept plain-text instructions, increasing tool-design flexibility.

4-3. Multimodal understanding & spatial reasoning (MMMU 84.2%, etc.)

It scores 84.2% on MMMU, which includes images, charts, and video tasks. It handles chart reading, slide summarization, UI-mock critiques—organizing non-text data at a practical level. It’s solid on science/research sets like ERQA and CharXiv. This helps with verbalizing visuals (e.g., drafting effective alt text).

4-4. Math, science, and health (AIME’25 94.6% / GPQA Diamond 88.4% [Pro])

On AIME’25 it hit 94.6%; on GPQA (Diamond), GPT-5 Pro reached 88.4%. Health evaluations are markedly improved—useful for pre-visit prep and result comprehension—positioned as a “companion for understanding,” not a doctor replacement.

4-5. Accuracy & integrity (less hallucination, less sycophancy)

Anonymous-prompt tests in ChatGPT show ~45% fewer hallucinations vs. 4o, and Thinking shows ~80% fewer vs. o3. Sycophancy is also much lower vs. 4o due to post-training. Training to say “can’t do that” when it can’t, and monitoring for deceptive behavior, push responses toward helpful over overeager.

5. Using it in ChatGPT: plan differences and caps

ChatGPT’s default model is GPT-5. Plus/Pro/Team can pick “GPT-5” or “GPT-5 Thinking” in the model picker; Pro/Team can also access “GPT-5 Thinking Pro.” Caps include Free: 10 messages per 5 hours (auto-falls back to a mini variant on overage; Thinking once/day), Plus: 80 messages per 3 hours (mini fallback beyond caps), etc.—tuned to avoid blocking practical use. It’s a gradual rollout, so timing may vary slightly by account.

Note
Older models (4o/4.1/4.5/o3, etc.) are being phased out. Opening past threads migrates them to the GPT-5 family, so the response style may change. Voice mode remains 4o for now.

6. For developers: mastering “deep thinking” via the API

The API core is gpt-5 (plus mini/nano) and gpt-5-chat-latest. Three operational levers matter most:

1. reasoning_effort: adjust thinking cost (e.g., "low" | "medium" | "high"). Start small, escalate only where needed.
2. verbosity: control brevity vs. detail ("low" | "medium" | "high"). Great for human-readable logs/auditability.
3. Custom tools: plain-text I/O simplifies complex JSON schemas. Use grammar constraints (CFG) for safe, exact formats.

Also improved: parallel tool calls and high-precision retrieval from long text. Announced integrations span Microsoft 365 Copilot / GitHub Copilot / Azure AI, boosting fit with enterprise IT.

Mini implementation (Chat Completions + tool use skeleton):

POST /v1/chat/completions
{
  "model": "gpt-5",
  "reasoning_effort": "low",
  "verbosity": "medium",
  "tools": [
    {
      "type": "custom_tool",
      "name": "search_tickets",
      "input_format": "text"
    }
  ],
  "messages": [
    {"role":"system","content":"You are a customer support engineer."},
    {"role":"user","content":"Summarize yesterday’s SLA-breach inquiries and sort them by severity."}
  ]
}

Ops tips
– Start with "reasoning_effort":"low" for a fast overview; rerun only the contested parts with "high" to refine—balancing felt cost and accuracy.
– Use gpt-5-mini for routine automations and large volumes of light tasks. gpt-5-nano suits log reformatting and batch summarization at the edge.

7. Which model fits which job?

7-1. Fast decisions & concise structuring (non-reasoning / gpt-5-chat-latest)

• Good for: sales-deck outlines, meeting-note highlights, first-draft FAQs
• Why: Doesn’t over-reason, stays fast and consistent
• Prompt example: “From these minutes, extract only the sentences relevant to decisions, classify into ‘decided / pending / follow-ups’, and output as headings + bullets.”

7-2. Serious coding & design reviews (reasoning / gpt-5)

• Good for: cross-repo bug fixes; refactors that balance UI aesthetics and accessibility
• Why: Strong task completion (e.g., SWE-bench Verified 74.9%), practical for real jobs
• Prompt example: “Review our Form components per WAI-ARIA, fix label association / focus order with automated tests, produce a PR plan and a diff patch.”

7-3. High-volume, repetitive work (gpt-5-mini / gpt-5-nano)

• Good for: multilingual normalization of product descriptions, log summarization, meeting tag assignment
• Why: Solid accuracy at low cost; parallel tool calls preserve throughput
• Prompt example: “Deduplicate and consolidate these 1,000 FAQs, normalize into screen-reader-friendly short sentences; output CSV with title (≤20 chars) + body (≤90 chars).”

7-4. Research, validation, audit (gpt-5 + reasoning_effort:"high" / GPT-5 Thinking/Pro)

• Good for: medical/legal document review, evidence-based decisions, long-horizon consensus building
• Why: Fewer hallucinations and better evidence structuring; Pro is even more detailed
• Prompt example: “Compare three papers on this treatment choice by assumptions / methods / results / limitations. Flag potential biases and external validity. Explicitly mark uncertainty as ‘deferred’.”

8. Accessibility-first practical samples

Sample A: Auto-generate alt text (image → description)
Goal: Create screen-reader-friendly descriptions for e-commerce product images
Prompt example:
“Write alt text ≤120 chars. Include color, shape, and use case; avoid subjective language; add one line for keyboard users’ focus order.”
Expected gist: “Black, round wireless speaker with mesh front and top physical buttons; USB-C charging. All buttons on top; Tab moves volume → play/pause → power.”

Sample B: Optimizing form error messages
Goal: Make errors short and actionable
Prompt example: “Rewrite each error into ‘what / why / how to fix’, ≤30 chars each.”
Expected gist: “Postal code missing. Enter 7 digits.”

Sample C: “Easy-to-grasp” meeting summaries
Goal: Conclusion-first summaries that reduce cognitive load
Prompt example: “Summarize into ‘Decisions / Concerns / To-dos’—≤3 items each. Add parenthetical notes for jargon.”
Expected gist: “Decisions: Adopt Plan A (verified by A/B test = comparative experiment). Concerns: F-pattern scanning disrupted. To-dos: Review ARIA attributes for input aids.”

Why GPT-5?
– Good at applying a consistent standard across long contexts and many items.
– Obeys instruction granularity (char limits, vocabulary constraints).
– In Thinking mode it’s better at “deferring when uncertain.” {index=16}

9. Risks & cautions: Design for trust, not blind faith

• Hallucinations are “reduced,” not “zero.” Double-check critical decisions (extra sources + human review). Prompt for citations and explicit uncertainty.
• Sycophancy is lower, but it can still lean toward decision-makers’ preferences. Standardize requests for counter-arguments.
• Medical/legal: keep it to “assist understanding,” and require expert judgment. GPT-5 itself will remind you of this boundary.
• Tool use: Apply least privilege and require a preamble/summary for confirmation before execution. Ensure auditable logs with verbosity:"high".

10. Who benefits most? — Personas & outcomes

• Product/Web teams: End-to-end help across plan → implement → validate for UI & accessibility; balances aesthetics + usability; stronger code-review rationale.
• Technical writers/PR: Faster conclusion-first outlines and plain-language rewrites; improves completion and comprehension rates.
• Customer support/Operations: As τ²-bench suggests, multi-tool orchestration reaches the finish line; better first-response coverage and consistent escalation.
• Research/Audit/QA: Easier long-form evidence tracking and appropriate deferrals; documents stand up to risk accountability.
• Health-literacy educators: Helps craft neither-too-much-nor-too-little explanations with examples for patients.

11. Accessibility level (how this content is designed)

Assuming diverse reader abilities and cognitive profiles, we emphasize:

• Inverted pyramid: Key points → background → detail, with bulleted TL;DR up front.
• Short sentences / plain(er) language: Balanced use of scripts; glossed jargon in parentheses.
• Heavy use of lists/tables: Minimizes eye movement; clear boundaries for screen readers.
• Concrete samples: Hands-on examples (alt text, error copy, meeting summaries) bridge understanding.
• Audience clarity: Role-specific usefulness is explicit for easier decision-making.

Overall: High readability akin to “AA-level” considerations (aligned with WCAG principles—perceivable, operable, understandable, robust—focused on practical readability rather than formal conformance).

12. Bottom line — “Fast, accurate, and actually thinks” is the new default

• GPT-5 auto-blends fast replies with deep reasoning in one experience—handling everyday writing/summaries, serious coding, and long-horizon deliberations from a single interface.
• The API splits into gpt-5 / mini / nano for fit-for-purpose use. With reasoning_effort and verbosity, you can tune speed × accuracy × explainability.
• Hallucinations and sycophancy are curbed, and “defer when unsure” is stronger—making auditable operations easier.
• Adoption tip: Start small; deepen reasoning stepwise. Use mini/nano for the long tail; bring in gpt-5 or Thinking/Pro only for hard parts—stabilizing cost and quality.

“Built-in, everyday smartness you can plug into work tomorrow.” — That’s GPT-5.

Appendix: Model comparison (quick reference)

Pricing per 1M tokens. Figures/specs may change in the future.