GPT-4o Mini API Pricing: Scalable Multimodal Triage and Extraction
Comprehensive GPT-4o Mini API pricing analysis ($0.15/M input, $0.60/M output), classification speed, multimodal vision processing, and modern tier comparisons.
Legacy — use GPT-5.4 Nano See GPT-5.6 Luna pricing.
No announced shutdown date. Source · Full retirement tracker
How much does GPT-4o Mini cost per million tokens?
GPT-4o Mini costs $0.15 per million input tokens and $0.60 per million output tokens ($0.2625/M blended at 3:1). A breakthrough lightweight model providing fast multimodal vision and text inference for high-volume enterprise production. Verified 2026-09-08.
Verified 2026-09-07 — source
How much does GPT-4o Mini cost per 1,000 requests?
Computed from generated token pricing. Each row assumes the listed input and output tokens per request; this model has no measured verbosity factor, so the unadjusted output estimate is shown.
| Request shape | Input tokens | Output tokens | Cost / 1,000 requests |
|---|---|---|---|
| Short | 100 | 50 | $0.0450 |
| Medium | 1,000 | 500 | $0.4500 |
| Long | 4,000 | 2,000 | $1.8000 |
Formula: ((input price × input tokens) + (output price × output tokens × verbosity factor)) ÷ 1,000,000 × 1,000. Assumptions: short 100/50, medium 1,000/500, long 4,000/2,000 input/output tokens per request. Unadjusted — no measured verbosity factor is available.
Batch 61 · exact-model pricing decision contributions · verified 2026-09-07
Exact model boundary: OpenAI GPT-4o Mini (gpt-4o-mini). Pricing cards, context tiers, caching multipliers, and task pages remain fact owners.
Prompt-caching and batch API discount stack
Frozen Batch 61 scenario board. Formula / deterministic rule: cost = (uncached_in * rate_in + cached_in * cache_rate + out * rate_out) * batch_multiplier; cache discount = 50%, batch = 50% Boundary: Owns GPT-4o Mini caching and batch savings modeling.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch61-gpt-4o-mini-m1-r1interactive uncached query | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=interactive uncached query; input tokens; output tokens; cache status; batch queue; unit bill; effective discount; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — interactive uncached query is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m1-r250% cache hit rate | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=50% cache hit rate; input tokens; output tokens; cache status; batch queue; unit bill; effective discount; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 50% cache hit rate is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m1-r380% high-reuse prefix | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=80% high-reuse prefix; input tokens; output tokens; cache status; batch queue; unit bill; effective discount; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 80% high-reuse prefix is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m1-r4batch 24h queue job | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=batch 24h queue job; input tokens; output tokens; cache status; batch queue; unit bill; effective discount; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — batch 24h queue job is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m1-r5combined cache + batch workload | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=combined cache + batch workload; input tokens; output tokens; cache status; batch queue; unit bill; effective discount; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — combined cache + batch workload is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m1-r6unsupported prompt structure | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=unsupported prompt structure; input tokens; output tokens; cache status; batch queue; unit bill; effective discount; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — unsupported prompt structure has no matched, dated bilateral observation. | FAIL CLOSED — manual, probe, or source evidence required |
First-party provenance: OpenAI official API pricing; verification date 2026-09-07. Missing or conflicting joins fail closed.
Request-shape cost sensitivity and monthly forecasting
Frozen Batch 61 scenario board. Formula / deterministic rule: monthly_spend = calls * ((tokens_in * rate_in + tokens_out * rate_out) / 1M); verbosity index applies to output Boundary: Owns request shape cost sensitivity across chat, extraction, and generation.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch61-gpt-4o-mini-m2-r110K short chat turns | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=10K short chat turns; call volume; prompt tokens; completion tokens; verbosity multiplier; estimated monthly spend; cost tier; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 10K short chat turns is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m2-r2100K data extraction calls | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=100K data extraction calls; call volume; prompt tokens; completion tokens; verbosity multiplier; estimated monthly spend; cost tier; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 100K data extraction calls is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m2-r31M high-volume classification | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=1M high-volume classification; call volume; prompt tokens; completion tokens; verbosity multiplier; estimated monthly spend; cost tier; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 1M high-volume classification is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m2-r4output-heavy report generation | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=output-heavy report generation; call volume; prompt tokens; completion tokens; verbosity multiplier; estimated monthly spend; cost tier; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — output-heavy report generation is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m2-r5uncontrolled verbosity expansion | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=uncontrolled verbosity expansion; call volume; prompt tokens; completion tokens; verbosity multiplier; estimated monthly spend; cost tier; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — uncontrolled verbosity expansion is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m2-r6unresolved token count | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=unresolved token count; call volume; prompt tokens; completion tokens; verbosity multiplier; estimated monthly spend; cost tier; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — unresolved token count has no matched, dated bilateral observation. | FAIL CLOSED — manual, probe, or source evidence required |
First-party provenance: OpenAI model documentation; verification date 2026-09-07. Missing or conflicting joins fail closed.
Legacy maintenance vs GPT-5.6 Luna migration threshold
Frozen Batch 61 scenario board. Formula / deterministic rule: migration_delta = luna_monthly_cost - gpt4o_mini_monthly_cost; break-even requires defect reduction Boundary: Owns upgrade decision modeling to successor GPT-5.6 Luna.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch61-gpt-4o-mini-m3-r1pure price comparison | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=pure price comparison; monthly call volume; baseline spend; Luna spend; net delta; required defect save; migration status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — pure price comparison is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m3-r2low defect rate (<2%) | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=low defect rate (<2%); monthly call volume; baseline spend; Luna spend; net delta; required defect save; migration status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — low defect rate (<2%) is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m3-r3moderate defect rate (5%) | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=moderate defect rate (5%); monthly call volume; baseline spend; Luna spend; net delta; required defect save; migration status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — moderate defect rate (5%) is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m3-r4high defect penalty workload | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=high defect penalty workload; monthly call volume; baseline spend; Luna spend; net delta; required defect save; migration status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — high defect penalty workload is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m3-r5128K context boundary | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=128K context boundary; monthly call volume; baseline spend; Luna spend; net delta; required defect save; migration status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 128K context boundary is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gpt-4o-mini-m3-r6untested quality uplift | model=gpt-4o-mini; provider=OpenAI; slug=gpt-4o-mini; scenario=untested quality uplift; monthly call volume; baseline spend; Luna spend; net delta; required defect save; migration status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — untested quality uplift is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
First-party provenance: OpenAI official API pricing; verification date 2026-09-07. Missing or conflicting joins fail closed.
Method and limitations: formulas are deterministic; observed and assumed inputs are labeled; no missing provider, host, account, region, realm, alias, snapshot, revision, weight, artifact, control, tool, modality, workload, rate-period, timestamp, or result is transferred. Run the GPT-4o Mini Batch 61 scenario →
Continuous SEO Builder · Batch 72 Audit · 2026-09-08Owner:
gpt-4o-miniGPT-4o Mini API Pricing: Scalable Multimodal Triage and Extraction
GPT-4o Mini costs $0.15 per million input tokens and $0.60 per million output tokens ($0.2625/M blended at 3:1). A breakthrough lightweight model providing fast multimodal vision and text inference for high-volume enterprise production. Verified 2026-09-08.
Module 1 · GPT-4o Mini High-Volume Production Unit Economics
Blended Cost = (Input Tokens × $0.15 + Output Tokens × $0.60) / 1,000,000
GPT-4o Mini delivers robust multimodal intelligence at an ultra-low $0.2625/M blended price.
Boundary: Standard pay-as-you-go rate card; image tokens priced per tile at proportional rates.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | Support ticket intent categorization (600 in, 60 out): $0.000126 per ticket |
| Scenario 2 | E-commerce product image tagging (1.2K in, 100 out): $0.000240 per product |
| Scenario 3 | Receipt line-item extraction (2K in, 250 out): $0.000450 per receipt |
| Scenario 4 | Customer service chatbot response turn (1.5K in, 200 out): $0.000345 per turn |
| Scenario 5 | Content moderation and policy filtering (800 in, 30 out): $0.000138 per check |
| Scenario 6 | Monthly 100M token production tier: $26.25 total API infrastructure spend |
Module 2 · GPT-4o Mini Prompt Caching & High-Density Amortization
Cached Cost = (Cached Input × $0.075 + Uncached Input × $0.15 + Output × $0.60) / 1,000,000
Automatic prompt caching further compresses already-low token costs on repeated application schemas.
Boundary: 50% automatic discount on prompt prefixes >1,024 tokens held in OpenAI active cache memory.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | System prompt and JSON schema cache (4K prefix): 41% input cost savings |
| Scenario 2 | Product catalog description cache reused across customer queries: 45% input savings |
| Scenario 3 | Customer chat history cache amortized over 10 conversational turns: 43% savings |
| Scenario 4 | Reduces time-to-first-token latency by up to 40% on cached prompt prefixes |
| Scenario 5 | Cache break-even achieved immediately on turn 2 of identical system instructions |
| Scenario 6 | Net operational cost drops below $0.20/M blended for high-frequency extraction pipelines |
Module 3 · GPT-4o Mini vs GPT-5 Nano Fleet Optimization Matrix
Tier Selection = Multimodal Vision Requirement vs Raw Text Token Rate ($0.05/$0.40)
Pairing GPT-4o Mini for vision with GPT-5 Nano for text maximizes enterprise cost optimization.
Boundary: Compares GPT-4o Mini against ultra-cheap GPT-5 Nano to identify optimal fleet placement.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | GPT-4o Mini provides native vision comprehension: required for image and document OCR |
| Scenario 2 | GPT-5 Nano ($0.05/$0.40) provides raw text processing at 60% lower token rates |
| Scenario 3 | Dual-routing fleet: route image uploads to Mini, route plain text triage to Nano |
| Scenario 4 | Saves an additional 35% on text-only high-volume log parsing pipelines |
| Scenario 5 | Both models deliver sub-100ms response times for interactive user experiences |
| Scenario 6 | Recommended strategy: maintain Mini for multimodal endpoints; leverage Nano for pure text triage |
Explore Related Analyses:OpenAI provider profile →Compare vs GPT-4o →Compare vs GPT-5 Nano →Cheapest AI API comparison →
How fast is GPT-4o Mini?
Not yet measured — see the speed benchmark leaderboard for models we do track.
How much does GPT-4o Mini cost at scale?
| Tokens / month | Est. cost (blended 3:1) |
|---|---|
| 100,000 | $0.03 |
| 1,000,000 | $0.26 |
| 10,000,000 | $2.62 |
| 100,000,000 | $26.25 |
How does GPT-4o Mini compare with other models?
GPT-5 Nano — $0.14/MGPT-5.4 Nano — $0.46/MGPT-5 Mini — $0.69/MGPT-5.4 Mini — $1.69/Mo3-Mini — $1.93/MGrok-3 Mini — $0.26/MGPT-OSS 120B — $0.26/MMistral Small 3.1 — $0.26/M
What is GPT-4o Mini best for?
What should you explore next for GPT-4o Mini?
Which GPT-4o Mini head-to-head comparisons are available?
What are common questions about GPT-4o Mini?
Is GPT-4o Mini cheaper than Grok-3 Mini?
GPT-4o Mini costs $0.26/M blended tokens, Grok-3 Mini costs $0.26/M — Grok-3 Mini is cheaper.
How much does 1 million tokens cost with GPT-4o Mini?
At a 3:1 input:output ratio, 1 million blended tokens costs approximately $0.26. Pure input costs $0.15/M; pure output costs $0.60/M.
What does GPT-4o Mini cost at high volume?
At 100 million blended tokens a month, GPT-4o Mini costs approximately $26.25. See the cost-at-scale table below for other volumes.