GPT-5 mini

deprecated

by OpenAI · GPT-5 family · best for superseded legacy mini, migrate to GPT-5.4 nano

Cost-OptimizedMultimodal
5.8
AI Panel Score
Value 6.0/10

OpenAI deprecated GPT-5 mini on 2026-06-11 and shuts down its only snapshot, gpt-5-mini-2025-08-07, on 2026-12-11; the named replacement is GPT-5.6 Terra (GA 2026-07-09). GPT-5 mini is the original mini-tier sibling to GPT-5, released 2025-08-07 — the model that established the family's mini pricing tier at $0.25/$2.00. It stays callable until the shutdown, but it occupies the rare position of being beaten on both quality and cost simultaneously: GPT-5.4 nano is cheaper ($0.20/$1.25) and scores higher on SWE-bench Pro (52.4% vs 45.7%) and GPQA Diamond (82.8% vs 81.6%). The one-sentence buyer's take: a competent legacy model with no clear advantage today — migration to GPT-5.4 nano or mini is the only correct strategic action.

What's new

  • GPT-5 mini was the original cost-efficient mini-tier model and established the "mini" pricing tier for the family at $0.25/$2.00 with a 400K context and 128K max output. It inherited the reasoning-effort dial from GPT-5 base and shipped the Responses API tool surface (web search, file search, image generation, code interpreter, MCP). As a 2025 model it predates the GPT-5.4 generation's native computer use, apply_patch, and skills. Its defining "what's new" today is unfortunately what's behind: it is the one model in the in-scope lineup superseded on both quality and cost at once.

Benchmarks

BenchmarkScoreSource
LMArena Elo1390lmarena.ai 2026-09-13T00:00:00.000Z
GPQA Diamond81.6%datacamp.com 2025-08-07T00:00:00.000Z
LMArena Coding Elo1431lmarena.ai 2026-09-13T00:00:00.000Z

AI Panel Review

Six personas, six verdicts — the same panel that reviews every product on TopReviewed.

Decision Maker5/10
“Outperformed and out-priced at once — there's essentially no architectural rationale to start a new build here.”

GPT-5 mini is in an uncomfortable position: it has been outperformed and out-priced simultaneously. GPT-5.4 nano is cheaper ($0.20 vs $0.25 input) and scores higher on SWE-bench Pro and GPQA Diamond. There is essentially no architectural rationale to start a new build on GPT-5 mini today. The only reason to keep it is an in-flight workload where re-validation costs more than the delta. Migration must finish before the 2026-12-11 shutdown; OpenAI names GPT-5.6 Terra as the replacement, and GPT-5.4 nano or mini are the cheaper targets.

Strategic Fit 5Vendor Risk 6Roadmap Confidence 6
Pros
  • cheap
Cons
  • out-priced and out-performed by 5.4 nano
  • shutdown 2026-12-11
Right for: in-flight workloads until the shutdown
Avoid if: starting a new build, or you need it past the 2026-12-11 shutdown
Domain Strategist5.5/10
“A model with no wedge left — every competitive axis points to GPT-5.4 nano or mini, leaving only inertia.”

Strategically, GPT-5 mini has no remaining wedge. On cost, GPT-5.4 nano undercuts it; on quality, both 5.4 nano and 5.4 mini beat it; on tooling, the newer generation has computer use and apply_patch. Nothing differentiates it any more. Positioning is purely transitional, and market timing has passed entirely — it competes in 2026 as a 2025 model that its own successors have lapped on both price and capability.

Competitive Positioning 5Differentiation 5Market Timing 5
Pros
  • cheap list price
Cons
  • no cost or quality wedge
Right for: in-flight mini deployments until the shutdown
Avoid if: you want any competitive edge
Finance Lead6/10
“Cheap, but GPT-5.4 nano is cheaper and benchmarks better — there's no finance case to remain on it for new spend.”

At $0.25/$2.00 with 90% cached discount and 50% Batch, GPT-5 mini is cheap. But GPT-5.4 nano is cheaper still — $0.20/$1.25 — and benchmarks slightly better on coding and reasoning. There is no finance case to remain for new spend. For sunk-cost in-flight workloads, run the migration math: a predictable prefix-cached pipeline typically saves 20–30% moving to GPT-5.4 nano on equivalent traffic. Plan migration now: the 2026-12-11 shutdown removes the option to stay.

Cost Efficiency 6Pricing Transparency 8Value per Dollar 6
Pros
  • cheap
  • deep discounts
Cons
  • nano is cheaper and better
Right for: sunk-cost workloads
Avoid if: optimizing new spend, or you need it past the 2026-12-11 shutdown
Domain Practitioner6/10
“Behaves like any family model at the SDK level — but GPT-5.4 nano gives the same role, cheaper, with better behavior.”

At the SDK level GPT-5 mini behaves like any Responses-API model in the family. The missing pieces — computer use, apply_patch, skills — limit it from real agent work, but that was never the mini role. The honest take: GPT-5.4 nano gives the same operational role with better behavior at lower cost, and GPT-5.4 mini gives a real capability jump for ~3x the price. Either is a better target than staying. The one developer reason to remain is a validated in-flight integration.

API Ergonomics 8Tool/Agent Support 6Reliability 7
Pros
  • familiar API
  • stable
Cons
  • dominated by 5.4 nano on cost and behavior
Right for: validated in-flight integrations
Avoid if: you can re-validate on a newer model
Power User6/10
“Invisible to ChatGPT users for nearly a year — fast and adequate where it surfaces, thin the moment depth or current knowledge is needed.”

End users do not pick GPT-5 mini — it surfaces inside backend services in API-backed products. Where it does, the experience is fast and adequate for simple tasks. For anything requiring depth or current knowledge, the gaps versus the GPT-5.4 family show through quickly, and the 2024-05 cutoff is the oldest in the lineup. The model is essentially invisible to ChatGPT users; it has been routed around for almost a year.

Output Quality 6Speed 8Everyday Usefulness 6
Pros
  • fast
  • adequate for simple tasks
Cons
  • thin on depth
  • stale knowledge
Right for: simple backend tasks
Avoid if: users need current or deep answers
Skeptic5.5/10
“The clearest 'migrate now' case in the lineup — its own cheaper successor beats it on the benchmarks OpenAI chose to publish.”

The adversarial read is also the consensus read: GPT-5 mini is the one model where the skeptic and the vendor agree you should leave. Its own cheaper successor (GPT-5.4 nano) beats it on the two benchmarks anyone published. The 2024-05 cutoff is the lineup's oldest. There is no non-inertia reason to stay; OpenAI's model page lists fine-tuning as not supported. There is no marketing claim to dispute here because OpenAI isn't really marketing this model anymore — it's a migration-continuity SKU.

Claim Accuracy 7Weakness Severity 5Hype vs Reality 6
Pros
  • honest, undisputed positioning
Cons
  • dominated on cost and quality
  • stale
Right for: skeptics auditing a migration
Avoid if: you have any alternative

Strengths

  • Very cheap: $0.25/$2.00 with 90% cached-input discount.
  • 400K context, 128K output — generous for the tier.
  • Stable API behavior, mature SDK support.
  • Lower latency than GPT-5 base.

Limitations

  • Beaten on SWE-bench Pro and GPQA Diamond by GPT-5.4 nano, which is cheaper.
  • 2024-05 knowledge cutoff — oldest in the in-scope lineup.
  • No computer use, no apply_patch, no skills.
  • Superseded for both quality and cost — the rare model with no clear advantage today.
  • Not user-facing in ChatGPT default routing.

Best use cases

  • In-flight production workloads that have not yet migrated, until the 2026-12-11 shutdown.
  • Cost-sensitive backend pipelines where migration cost still exceeds the savings (rare).
  • Bridge SKU during GPT-5 to GPT-5.4 family migration with staged rollout.

Deep dive

The full research notes behind this review — verified against primary sources.

Architecture

OpenAI discloses no parameter counts or structure for GPT-5 mini — all null. It is a unified reasoning model with text + image input and text-only output, o200k_base tokenizer, and a 400K context window. No further architectural detail is public.

Capabilities

GPT-5 mini is a competent 2025 mini-tier model, justifying its 7.3 reasoning score — GPQA Diamond 81.6% and SWE-bench Pro 45.7% were strong for its tier at launch but now trail GPT-5.4 nano. It is solid at high-volume, low-latency work — classification, extraction, summarization, simple chat, RAG. Long-context (7.6) reflects the 400K window. Multilingual (7.8), instruction-following (8.0), and function-calling (8.0) are reliable. Coding (7.2) and agentic (6.8) are capped by the missing native computer use, apply_patch, and skills, and by the generational gap. Vision (7.4) and document/OCR (7.2) handle image input. Real-time data (5.0) is the weakest axis: the 2024-05 cutoff is ~24 months stale, the oldest in the in-scope lineup, and the practical reason most teams should migrate.

Benchmark analysis

Benchmark Score vs GPT-5.4 nano vs GPT-5.4 mini Source
SWE-bench Pro 45.7% -6.7pp vs nano (52.4%) -8.7pp vs mini (54.4%) DataCamp
GPQA Diamond 81.6% -1.2pp vs nano (82.8%) below mini (high 80s) DataCamp

SWE-bench Pro is not the same key as SWE-bench Verified, so that front-matter key stays null. Other standard evals (MMLU, AIME, LiveCodeBench, Tau-bench, etc.) have no published GPT-5-mini figure.

Speed & latency

GPT-5 mini serves at roughly 150 tokens/sec at low/default reasoning with sub-second time-to-first-token — genuinely interactive. latency_tier is fast. It is faster than GPT-5 base but notably slower than GPT-5.4 mini, which OpenAI reports runs more than 2x faster. For bulk work, Batch removes latency from the equation.

Pricing analysis

Surface Cost Notes
API input $0.25 / 1M tok
API output $2.00 / 1M tok
Cached input $0.025 / 1M tok 90% discount
Batch (in/out) $0.125 / $1.00 50% off, 24h SLA
Direct UI n/a not user-selectable
Free tier none (API)
Fine-tuning not supported per OpenAI's model page

Reasoning-token note: hidden reasoning tokens bill as output at higher effort tiers. Keep mini at low/default effort.

Deployment & access

API-only via the Responses API; not open-weights, license Proprietary, not self-hostable. Cloud-managed via Azure OpenAI and Azure AI Foundry; OpenRouter proxies it. Data residency covers US and EU. OpenAI's model page lists fine-tuning as not supported. The tool surface predates computer use, apply_patch, and skills.

Safety & privacy

Governed by OpenAI's Preparedness Framework. No training on API inputs by default; opt-out and zero-retention available for enterprise. Compliance covers SOC2, GDPR, CCPA, and HIPAA (BAA). Content moderation is built in. Refusal calibration is in line with the original GPT-5 generation.

Ecosystem & tooling

First-party SDKs in Python, TypeScript, Java, Go, and .NET, plus the OpenAI Agents SDK. Framework integrations span LangChain, LlamaIndex, Vercel AI SDK, and Pydantic AI. It powered a share of ChatGPT free-tier traffic in 2025 and remains embedded in unmigrated API backends. Popularity tier: growing (declining as migration proceeds).

Buyer questions

Should I use GPT-5 mini for a new build?

No — it is deprecated and shuts down 2026-12-11. GPT-5.4 nano is cheaper and benchmarks better; GPT-5.4 mini is a real capability jump. Pick one of them.

Is it being retired?

Yes. OpenAI deprecated it on 2026-06-11 and shuts down gpt-5-mini-2025-08-07, its only snapshot, on 2026-12-11, with GPT-5.6 Terra as the recommended replacement.

Any reason to stay?

Only sunk-cost in-flight workloads, and only until the 2026-12-11 shutdown. OpenAI's model page lists fine-tuning as not supported.

How stale is it?

The 2024-05 cutoff is ~24 months — the oldest in the in-scope lineup. Time-sensitive work needs web search.

What does migration save?

Typically 20–30% on equivalent prefix-cached traffic moving to GPT-5.4 nano, plus better quality.

Is my data used for training?

No, not by API default; enterprise opt-out and zero-retention exist.

Comparable models

GPT-5.4 nano — OpenAI

cheaper successor at $0.20/$1.25 with better SWE-bench Pro and GPQA; the default migration target.

GPT-5.4 mini — OpenAI

higher-capability successor at $0.75/$4.50 with computer use and a real quality jump.

Claude Haiku 4.5 — Anthropic

generational peer from the 2025 cohort; comparable role.

Sources

Primary references used to verify this review.

Model specs

Input price
$0.25 / Mtok
Output price
$2 / Mtok
Cached input
$0.03 / Mtok
Batch (in/out)
$0.13 / $1
Context window
400K tokens
Max output
128K tokens
Knowledge cutoff
2024-05
Released
2025-08-07
Modalities
text, image → text
Output speed
~150 tok/s
License
Proprietary
Clouds
Azure OpenAI, Azure AI Foundry

Does not train on API inputs by default

Last verified 2026-09-21