LLM output prices fell about 94% since 2023: how to cut your AI bill without losing quality (2026)

Summary. Frontier large language model output prices have fallen roughly 94.5% since March 2023, according to Epoch AI, yet most teams have not touched their model choice, so they are paying 2026 flagship prices for work a cheaper model now does. The spread is enormous: DeepSeek V4 Flash costs $0.28 per 1M output tokens, while GPT-5.6 Sol costs $30 for the same 1M, a 100x gap. On top of that, models are being retired: DeepSeek pulls the deepseek-chat and deepseek-reasoner names on July 24, 2026, and OpenAI shipped the three-tier GPT-5.6 family (Luna at $1/$6, Terra at $2.50/$15, Sol at $5/$30) on July 9, 2026. The opportunity is to migrate off deprecated or overpriced models and route each task to the cheapest model that still clears its quality bar. This is the work eCorpIT does as a migration and cost-optimisation service, and this article explains the levers, the trade-offs, and where a partner helps.

The trap is treating a model as a permanent decision. Teams pick a flagship during a prototype, wire it into everything, and never revisit it while the market halves prices twice underneath them. The result is an AI bill that grows with usage and never with value. Cutting it is not about buying the cheapest model everywhere; it is about matching each workload to the right tier and removing the models that are about to break.

What actually happened to LLM prices in 2026

Two forces are moving at once: a long price decline and a sudden fragmentation into tiers. Epoch AI tracks the decline, with a frontier output-price index falling from 100 in March 2023 to about 5.5 in 2026. The fragmentation is newer: providers now ship small, medium, and large variants of the same family at very different prices, so the model name alone no longer tells you the cost. The table below lists current published API prices, per 1M tokens, as of July 2026.

Model (July 2026)	Input per 1M	Output per 1M
Gemini 3.1 Flash	$0.10	$0.40
DeepSeek V4 Flash	$0.14	$0.28
DeepSeek V4 Pro	$0.435	$0.87
GPT-5.6 Luna	$1.00	$6.00
GPT-5.6 Terra	$2.50	$15.00
Claude Opus (current series)	$5.00	$25.00
GPT-5.6 Sol	$5.00	$30.00
Claude Fable 5	$10.00	$50.00

The sticker price is only half the story, and the more important half is easy to miss. As developer and writer Simon Willison noted when GPT-5.6 launched, "price-per-million tokens doesn't tell us much now that the number of reasoning tokens can differ so much between models for the same task." A model with a low output price that burns 4x the reasoning tokens on your task can cost more than a pricier model that answers in one pass. The only number that matters is cost per completed task on your own traffic, which is why measurement, not a pricing page, drives real savings. Our note on GPT-5.6 inference cost for enterprise AI works through that distinction in detail.

Why you are probably overpaying

Three patterns account for most wasted AI spend. The first is the default-flagship habit: one strong model handles every request, including the 70% of calls that a model 20x cheaper would answer identically. The second is stale model choice: a workload pinned to a 2025 flagship while 2026 tiers deliver the same quality for a fraction of the price. The third is deprecation drift, where a model you depend on is retired and a blind rename silently changes both quality and cost. DeepSeek's July 24, 2026 cutoff is a live example, and we covered the exact remap in our DeepSeek V4 migration guide.

None of these are exotic. They are the natural result of shipping fast and not revisiting a decision that the market keeps changing. The fix is a repeatable process, not a one-time model swap.

The levers that actually cut an AI bill

There are five levers, and the biggest wins usually come from the first two. Routing captures the tier spread. Migration removes retired and overpriced models. The rest tighten what each call costs.

Lever	What it does	Where the saving comes from
Route by task difficulty	Sends each request to the cheapest model that passes its quality check	The 5x to 100x gap between model tiers
Migrate off deprecated or legacy models	Replaces retired or overpriced models with current, cheaper equivalents	Retired aliases and premium legacy pricing
Prompt caching and batching	Reuses cached input and batches non-urgent work	Cache reads are far cheaper than fresh input tokens
Right-size reasoning effort	Lowers the reasoning effort where quality holds	Reasoning tokens often dominate total cost
Trim context and use retrieval	Sends less input per call through retrieval instead of stuffing context	Input tokens scale directly with prompt size

Routing is the lever most teams underuse. In practice, a classifier or a rules layer sends simple requests to a Flash-class model and escalates only the hard ones to a premium tier, and a well-built router captures most of the tier spread without a human noticing a quality drop. This is a design decision with real trade-offs around latency and complexity, which we lay out in our LLM hybrid-routing and API-spend framework. Caching adds more than it looks: DeepSeek prices cache-hit input at $0.0028 per 1M against $0.14 for a cache miss, and OpenAI keeps a 90% discount on cached reads, so a workload with repetitive prompts can cut its input bill sharply with no quality change at all.

A worked example makes the routing gap concrete. Take a support workload generating 10M output tokens a month. Run entirely on GPT-5.6 Sol at $30 per 1M, that output costs about $300 a month. Route the 80% of requests that are routine to DeepSeek V4 Flash at $0.28 per 1M and keep the hard 20% on Sol, and the same output costs roughly 8M at $0.28 plus 2M at $30, about $62 a month before input and caching savings. That is close to an 80% cut on output, and the only thing standing between a team and that number is the evaluation work to prove the cheap model holds quality on the routine 80%. The figures are arithmetic on the listed July 2026 prices, not a quote, but the shape of the saving is typical.

A migration and optimisation process that holds quality

Cost work fails when it quietly degrades output, so the process has to protect quality as hard as it chases price. The sequence we use is deliberate.

1. Audit. Inventory every model call, its volume, token profile, and current cost, and find the deprecated or premium-tier calls first.
2. Measure cost per task. Replay real traffic against candidate models and record cost and quality per completed task, not per token.
3. Route. Introduce tiering so simple work lands on cheap models and only hard work escalates.
4. Evaluate. Gate every change behind an evaluation set so a cheaper model cannot ship if it fails your quality bar. Our AI evaluations and observability service is built for exactly this.
5. Migrate. Move off retired and overpriced models with the parameter and behaviour changes each new model needs.
6. Monitor. Watch cost and quality continuously, because the market and your traffic both keep shifting.

What eCorpIT builds, and who it is for

eCorpIT is a Gurugram, India software organisation, founded in 2021, with senior-led, multi-disciplinary engineering teams. We hold a CMMI Level 5 appraisal and are MSME registered, and we work across the major cloud and platform partners including AWS, Microsoft, Google, Shopify, and Kaspersky. Our LLM migration and cost-optimisation engagement covers the full sequence above: an audit of current model usage and spend, a cost-per-task measurement harness, a routing layer matched to your traffic, an evaluation gate so quality holds, and the migration itself off deprecated or overpriced models. We design these systems aligned with your data-protection requirements rather than claiming any blanket certification, and we hand over the routing and evaluation tooling so your team can keep tuning it.

This is for CTOs and engineering leaders whose LLM bill is now a real line item and whose traffic mixes easy and hard requests, and for teams facing a forced migration such as the DeepSeek cutoff. If you run models at scale but have not revisited the choice in months, the audit alone usually pays for itself. Where self-hosting a model beats an API for your volume, we also cover that path in our private LLM deployment service.

India-specific considerations

For teams in India, two points sit alongside the technical work. API spend is billed in US dollars, so a weaker rupee raises the effective cost even when token usage is flat; budgeting in dollars and reviewing the exchange exposure keeps the number honest. And routing decisions interact with data governance under the Digital Personal Data Protection Act, 2023: a cheaper model hosted outside India may move a cross-border data question to the front of the queue, so the routing layer should account for where data is allowed to go, not only for price. Our DPDP engineering playbook for Indian startups covers the controls that keep this defensible.

FAQ

How much have LLM prices actually fallen?

Epoch AI tracks frontier output prices falling roughly 94.5% since March 2023, with its index dropping from 100 to about 5.5. The decline is uneven across tasks, and reasoning-heavy work has fallen less than simple generation, so the exact saving depends on your workload rather than a single headline percentage.

Why not just switch everything to the cheapest model?

Because the cheapest model does not clear every quality bar, and per-token price hides real cost. A cheap model that uses more reasoning tokens on a hard task can cost more than a pricier one that answers in a single pass. The safe approach routes simple work to cheap models and escalates hard work, gated by evaluations.

What is model routing?

Routing sends each request to the cheapest model that can handle it, using a classifier or rules layer that escalates only harder requests to a premium tier. A well-built router captures most of the price gap between tiers without a visible quality drop, and it is the single lever that most often produces the largest saving on a real workload.

Why does migrating off deprecated models matter for cost?

Retired models force change on a deadline, and a blind rename can alter both quality and price. DeepSeek retires deepseek-chat and deepseek-reasoner on July 24, 2026, and their alias maps to a Flash-tier model, not the top tier. Migrating deliberately lets you pick the right tier instead of inheriting a default.

How does caching reduce the bill?

Caching reuses input the model has already processed. DeepSeek prices cache-hit input at $0.0028 per 1M against $0.14 for a cache miss, and OpenAI keeps a 90% discount on cached reads. Workloads with repetitive system prompts or shared context can cut their input cost with no change to output quality.

Will cutting cost hurt output quality?

Only if you skip the evaluation step. A disciplined process replays real traffic, measures quality per task for each candidate model, and gates every change behind an evaluation set, so a cheaper model cannot ship unless it meets your bar. Cost work without evaluations is where silent quality regressions come from.

Who should run this kind of optimisation?

Teams whose AI bill has become a real line item, whose traffic mixes easy and hard requests, or who face a forced migration such as the DeepSeek cutoff. If you run LLMs at scale and have not revisited the model choice in months, an audit usually surfaces quick savings, because the market has moved faster than most integrations.

Does eCorpIT lock us into specific models or tools?

No. The engagement builds a routing and evaluation layer around your workload and hands it over, so your team can keep tuning model choice as prices change. The aim is a repeatable process you own, not a dependency, because the cost-optimal model in July will not be the cost-optimal model by year end.

How eCorpIT can help

eCorpIT is a senior-led software organisation in Gurugram, founded in 2021, that builds and optimises production AI systems. Our LLM migration and cost-optimisation service audits your current model spend, measures cost per task on real traffic, builds a routing and evaluation layer that protects quality, and migrates you off deprecated or overpriced models, including the July 24, 2026 DeepSeek cutoff. We design aligned with your data-protection requirements and hand over tooling you own. If your AI bill has outgrown its value, contact us and we will scope an audit with you.

References

1. Epoch AI, "LLM inference prices have fallen rapidly but unequally across tasks." https://epoch.ai/data-insights/llm-inference-price-trends
2. DeepSeek API Docs, "Models & Pricing." https://api-docs.deepseek.com/quick_start/pricing
3. DeepSeek API Docs, "Change Log," legacy model deprecation July 24, 2026. https://api-docs.deepseek.com/updates
4. Simon Willison, "The new GPT-5.6 family: Luna, Terra, Sol," July 9, 2026. https://simonwillison.net/2026/Jul/9/gpt-5-6/
5. OpenAI, "Previewing GPT-5.6 Sol." https://openai.com/index/previewing-gpt-5-6-sol/
6. AI Pricing Guru, "GPT-5.6 Pricing: Sol $5, Terra $2.50, Luna $1 per 1M." https://www.aipricing.guru/openai-pricing/
7. CloudZero, "LLM API Pricing Comparison in 2026: Every Major Model, Ranked by Cost." https://www.cloudzero.com/llm-api-pricing-comparison/
8. Morph, "LLM API Providers (2026): 12 APIs Compared by Price per 1M Tokens." https://www.morphllm.com/llm-api
9. CostGoat, "LLM API Pricing Comparison and Cost Guide (Jul 2026)." https://costgoat.com/compare/llm-api
10. BenchLM, "LLM API Pricing History: How AI Model Costs Have Changed Over Time." https://benchlm.ai/llm-pricing-trends

Last updated: July 19, 2026.