AI Model Cost Effectiveness Index 2026

LLM pricing comparison by quality threshold and production workload

By Uvik Software | Edition 2

Choose the lowest-cost model configuration that meets your task’s quality and response-time requirements. A low token price is only one part of that decision. Reasoning length, failed attempts and human review can change which model costs least to use.

Uvik Software’s 2026 analysis separates three questions: the cost of reaching a benchmark threshold, the bill for a fixed token workload, and the operating cost of an accepted result. The first two use published data. The third shows an explicit scenario that you can replace with your own measurements.

Quick answer. In Uvik Software’s selected 32-configuration cohort, GPT-6 Luna at xhigh has the lowest benchmark cost at a score floor of 35, MiMo-V2.6-Pro at 45, GPT-6.1 Sol at high at 50, and Claude Opus 5.5 at xhigh with fallback at 56. These are shortlist results within the declared sample. Your acceptance rule and response-time limit determine which result is useful. [33] [5] [10] [13]

Figure 1. Uvik Software calculates the minimum budget at 11 benchmark thresholds. Each point is a selection scenario from the 17 October 2026 snapshot, not a historical observation.

Three findings matter for deployment. Reasoning settings can change cost within one model family. A cache discount applies only to eligible input, not the entire bill. Human correction can outweigh the inference charge. The sections below quantify each effect and state where the numbers are measured, calculated or illustrative.

AI model rankings by minimum quality

Read down the table until you reach the minimum benchmark score you need. Within the declared cohort, each row selects the qualifying configuration with the lowest published cost per benchmark task. Raising the floor can select a different model or a higher reasoning setting.

Sources: Artificial Analysis leaderboard and configuration pages. MiMo uses the leaderboard entry. Scores and costs are the publisher’s displayed rounded values. [5] [8] [9] [10] [11] [12] [13] [14] [30] [33]

A public score is a screening constraint, not a probability of success. The thresholds above are editorial scenarios. We use the publisher’s displayed rounded scores and costs, and select the cheapest qualifying row without assigning hidden weights. A configuration can be cost-efficient here and still be too slow, too unreliable or unsuitable for your application.

Methodology and comparison boundaries

This edition compares 32 priced configurations from 18 model families. We expanded the original sample with budget reasoning settings, Gemini 4 Argon and alternatives from DeepSeek, Kimi, Qwen, GLM, Grok and Mistral. The complete selection is disclosed in the two cohort tables below. It remains an editorial sample rather than a full market census; an omitted model can beat a listed option.

How the threshold index is calculated

For a score floor, retain cohort rows whose displayed score meets or exceeds that floor. Choose the smallest reported benchmark cost. Multiply by 1,000 to express a larger task budget. This is a cross-sectional threshold index, not a quality-adjusted inflation index. We do not divide a composite score by price or interpret a score of 50 as twice the capability of 25.

The source benchmark is Artificial Analysis Intelligence Index v4.3.2. Its cost measure accounts for the token categories used on each evaluation and weights the result by that evaluation’s contribution. It does not measure the cost of a successful customer outcome. The first-party API comparison later in the article is a separate dataset. [6] [7] [29]

What the index cannot establish

Displayed values are rounded. Small score differences are not evidence of statistical significance. Effort settings and fallback behavior remain separate configurations. Missing costs are excluded, never treated as zero. Public scores do not establish performance for a particular industry, language, integration or security requirement.

All values were checked on 17 October 2026. Retrieval date differs from evaluation date. Prices can change without a new model name, while aliases can resolve to newer models. Preserve provider, resolved model version, effort, endpoint, pricing mode and benchmark version when reproducing a result.

Disclosure. Uvik Software announced an Anthropic partnership in June 2026. This index applies the same published-cost selection rule across the declared cohort. Uvik Software compiled the data and calculations; Artificial Analysis ran the source benchmarks.

Benchmark dataset for economical configurations

The 17 rows below cost no more than $0.72 per published benchmark task. The grouping is for readability, not a recommendation. Lower cost is useful only when the configuration clears the acceptance and latency requirements of the application.

MiMo and GLM Flash rows use the leaderboard snapshot. Other source numbers identify configuration pages. Rows with identical rounded costs can differ in score or latency; a tie on one metric does not make the configurations interchangeable.

Benchmark dataset for higher spending configurations

The remaining 15 configurations broaden the comparison across current model families and reasoning settings. They may offer task-specific advantages that the broad index does not capture. The index does not recommend paying more without measuring that advantage.

Gemini 4 Argon is included at its published high reasoning setting. Google announced public API availability in October 2026; its introductory rate is time-limited. Its cost figure is not a reason to omit application testing or assume the same price will persist. [29] [49]

A useful counterexample to token-price rankings: Opus 5.5 at xhigh with fallback and Sonnet 5.5 at max with fallback both display a score of 56. Their benchmark costs are $3.46 and $5.46. Uvik Software calculates a 36.6% lower benchmark bill for the Opus configuration. The rounded score tie does not prove equal task-level quality. [13] [16]

Reasoning settings change both cost and waiting time

Figure 2. GPT-6.1 Sol configurations from Artificial Analysis. Benchmark task cost and the 500-token response-time probe use different workloads; the chart does not treat them as one experiment.

Moving from medium to max increases Sol’s displayed score from 48 to 52 and its benchmark cost from $0.21 to $0.72 per task. Uvik Software calculates a 3.43× cost increase. The separate response-time measure rises from 14.31 to 331.76 seconds, or 23.18×. Four additional index points can therefore have a material budget and waiting-time cost. [9] [10] [30] [11]

The useful setting depends on the task. A long-running research job may tolerate several minutes. An interactive workflow may need a lower effort setting or a faster configuration. Measure p95 completion time at your actual prompt length and concurrency before setting a user-facing promise.

A cheap configuration can be a slow configuration

Haiku 5.5 at xhigh and Sonnet 5.5 at medium with fallback both display a score of 41. Haiku’s benchmark cost is $0.12 versus $0.48, but their separate 500-token response measures are 79.39 and 6.25 seconds. That is a fourfold cost difference and about a 12.7-fold response-time difference. The comparison illustrates a trade-off; it does not establish equal answers or a production service level. [8] [42]

Need a model shortlist that meets both quality and response-time requirements? Discuss evaluation design with Uvik Software’s generative AI consulting team.

LLM API pricing comparison for 2026

This table answers a different question: what would the same token budget cost? Rates are USD per million native tokens, checked on 17 October 2026. The final column uses 1,000 requests, each with 2,000 uncached input and 500 total billed output tokens. It assumes no tools or retries.

Sources: first-party OpenAI, Anthropic, Google and DeepSeek pricing pages. Price conditions below are part of the comparison. [1] [2] [3] [4]

Conditions that change the bill

OpenAI rows use Standard short-context rates. Anthropic Haiku uses prompts at or below 100,000 tokens; larger prompts have different rates. Cache-read prices are shown for reference, but cache writes, storage and minimum cacheable lengths must be considered before claiming a saving. [1] [2]

DeepSeek rows use peak rates to avoid assuming off-peak scheduling. Its published off-peak token rates are half of peak. The Flash alias currently resolves to V4.1-Flash-1017. Pin the resolved version in your logs, because an unchanged alias can refer to an updated model. [4]

Google’s quoted rates are temporary: Argon uses $2 input and $10 output through 30 November 2026, then $4 and $20. Flash 3.8 uses $0.75 and $3.75 through 31 December, then $1.50 and $7.50. Argon supports implicit caching only. Gemini explicit-cache storage can add a separate charge. [3]

How workload mix changes cost

Figure 3. Uvik Software fixed-workload calculations. Standard prices include the stated Google promotions and DeepSeek peak rates. No tools, retries or human review.

In this 12-model sample, the short-response scenario ranges from $0.45 to $45 per 1,000 requests. That 100× spread measures list-price arithmetic. It says nothing about whether the cheapest option can complete the task.

Output-heavy workloads magnify output-token prices. For example, the same selected Gemini Flash quote produces $3.375 per 1,000 short-response requests and $16.50 for the output-heavy scenario. Both calculations use a fixed billable token budget, not an observed application workload. [3]

The CSV includes all 12 APIs under all three scenarios. Include chargeable reasoning tokens in billed output. Native tokenizers differ, so equal token budgets are not identical text workloads. Use production token logs for a budget forecast. Subscription plans and self-hosting are outside this price table. The animation changes workload assumptions, not the date.

Prompt caching and batch processing change the bill

Before replacing a capable model, check whether repeated input or asynchronous work can be priced more efficiently. A cache-hit discount affects eligible input tokens. It does not automatically discount output, tool calls or every request. Batch discounts exchange immediate responses for asynchronous processing. [47] [48]

Figure 4. Uvik Software scenario using Sonnet 5.5 rates. Each of 1,000 requests has 6,000 shared input tokens, 2,000 variable input tokens and 500 billed output tokens.

At standard uncached prices, this workload costs $21. Batch without caching costs $10.50. With standard caching, one initial write and 999 valid reads cost $9.6144, a 54.2% reduction. The calculation includes the first cache write, variable input and output. It assumes the shared prefix is eligible, identical and reused while the cache remains valid.

Calculation: first request = (6,000 × $2.50 + 2,000 × $2 + 500 × $10) / 1,000,000 = $0.024. Each later cache-hit request costs (6,000 × $0.10 + 2,000 × $2 + 500 × $10) / 1,000,000 = $0.0096. Total = $0.024 + 999 × $0.0096.

The example uses the five-minute cache-write price. Cache expiry or changed prefixes cause additional writes. Batch timing can also change cache-hit behavior, so the chart does not assume both discounts combine perfectly. Anthropic documents batch expiry after 24 hours for unfinished requests; this option suits work that can wait. [47] [48]

Track actual cache-read and cache-write tokens, batch expiry and completed requests. A provider’s advertised input discount is a rate, not a forecast of your total savings.

Long context can trigger a pricing threshold

Context capacity and context price are different constraints. A model may accept a long document while charging a higher rate above a prompt-length threshold. This can make a small prompt change materially alter the bill.

Figure 5. Uvik Software calculation from Anthropic’s 17 October 2026 Haiku 5.5 price tiers. Every request has 500 billed output tokens. The higher tier applies to the request, not only to the excess token.

At 100,000 input tokens, the illustrated workload costs $10.25 per 1,000 requests. At 100,001 it costs $51.2505, approximately five times as much. The change occurs because both input and output rates increase above the threshold. It is a property of this published price schedule, not a sudden increase in model capability. [2]

Count the complete prompt, including system messages, tool definitions and retrieved text. Anthropic states that cache-read and cache-write tokens count toward Haiku’s prompt-length threshold. Caching therefore does not bypass the length rule. Other models and providers use different conditions; this threshold must not be generalized to every API. [2]

For document applications, test retrieval depth and prompt compression alongside answer quality. Reducing context is useful only if evidence coverage and acceptance remain adequate. Log the priced prompt length before and after each optimization.

Cost per accepted output is the operating metric

A production model should be judged on the cost of work that meets an agreed acceptance rule. Add inference, tools, retrieval, retries, human review and allocated infrastructure costs, then divide by accepted outputs. Count rejected attempts in the numerator. Count an output only once after it passes the rule.

The denominator is accepted work. For support, define a grounded answer that follows policy and resolves the question. Our AI hallucination rates guide explains how to measure unsupported claims. For code, require tests, security checks and human acceptance. Set the rule before comparing models.

Figure 6. Uvik Software illustrative review-cost model. Review rates are assumptions. The example assumes reviewed outputs are corrected and every output is eventually accepted.

Worked example. A budget price profile costs $0.45 per 1,000 requests; a higher price profile costs $9. At $60 per hour, a one-minute review costs $1. The $8.55 API premium is recovered if the higher-priced profile avoids 8.55 reviews per 1,000 requests: a reduction of 0.855 percentage points.

If the budget profile needs 5% review and the alternative needs 1%, total costs become $50.45 and $19 per 1,000 accepted outputs. These review rates are illustrative assumptions, not measured results for Haiku, Sol, Sonnet or any other model. Replace them with your own data before making a deployment decision.

If review and retries dominate your bill, model routing and workflow design matter. Explore Uvik Software AI agent development services.

Routing and retries need their own economics

A model routing system can send straightforward requests to a lower-cost model and escalate difficult cases. Savings depend on the escalation rate, the cost of the routing decision and the failures the router fails to detect. Measuring only the model calls can hide the cost of incorrect acceptance.

A transparent cascade example

Assume every request first uses a $0.00045 small-model profile, the router costs $0.00005 per request, and an escalated request adds a $0.009 larger-model call. All-large inference would cost $9 per 1,000 requests. These are synthetic price profiles; no accuracy or escalation rate has been measured for them.

Expected inference cost per request = small-model cost + router cost + escalation fraction × larger-model cost. At 30% escalation, that is $0.00045 + $0.00005 + 0.30 × $0.009 = $0.0032. The resulting 64.4% inference saving is conditional arithmetic, not a Uvik Software client result or evidence of unchanged quality.

Why retry math often overpromises

If attempts were independent with success probability p and cost c, unlimited retry-until-success would cost c / p on average. Real model failures are often correlated: the same missing context, incorrect tool or ambiguous instruction can fail repeatedly. A retry can also grow the prompt and change its cost.

Use bounded retries, record every failed attempt and define when to escalate to a person. Compare workflows on accepted outcomes under the same latency and error-severity limits. A low average bill is not an adequate result if important failures pass unnoticed.

When self hosting belongs in the comparison

Self hosting adds a fixed-cost problem. An illustrative $8,000 monthly serving and operations budget, with negligible extra variable cost, needs 8 million accepted outputs to match an API costing $0.001 per accepted output. At $0.01, the break-even volume is 800,000. Neither number is a GPU quote or a capacity claim. The deployment must actually sustain that accepted throughput while meeting latency and availability requirements.

Need routing, evaluation and cost observability built into the workflow? Explore Uvik Software AI agent development services.

How to choose a model for your application

Shortlist by the work being accepted

This matrix is an evaluation plan. It does not claim that the cheapest configuration in one benchmark is best for coding, retrieval or your industry. Specialization, language coverage, context quality and integration requirements can outweigh a broad score.

Run a small evaluation before you migrate

1. Freeze a representative task set. Start with 100-200 real examples spanning common requests, difficult cases and known failure modes. Treat that as a pilot, not enough evidence for rare critical failures.
2. Compare exact configurations. Record provider, model version, reasoning effort, system prompt, tools, token counts, caching rules and concurrency. Keep workload and scoring rules consistent.
3. Grade blind where practical. Use deterministic checks for schemas and tests, plus rubric-based review for judgment tasks. Track acceptance, refusal quality, error severity and response time.
4. Measure the complete bill. Report cost per accepted output, p50 and p95 latency, retries, escalation rate and human minutes. Do not quietly omit unsuccessful attempts or cache warm-up costs.
5. Route only after testing. Send easy cases to the cheaper configuration and uncertain cases to a stronger one. Measure the router’s errors and its own cost. Keep a rollback option and a held-out test set.

When a larger model can earn its premium

A more expensive configuration is worth testing when it reduces review, avoids repeated tool calls or finishes difficult jobs in fewer attempts. A small model can remain the better choice for predictable tasks even if it loses a broad benchmark. The decision rule is whether the measured operating saving exceeds the extra inference and integration cost.

Need engineering capacity to run this evaluation and implement the result? Hire senior AI and ML engineers from Uvik Software.

What the price history can establish

Figure 7. Selected compact-model price observations. The first two are launch quotes and the 2026 value is a current quote. Models differ in capability, so this is not a quality-adjusted market index.

Applied to 2,000 input and 500 output tokens, the selected quotes produce $0.60 per 1,000 requests for GPT-4o mini in July 2024, $0.40 for GPT-4.1 nano in April 2025, and $0.45 for GPT-6 Luna in this October 2026 snapshot. The series describes three price observations. It cannot prove that useful AI became cheaper or more expensive at constant quality. [18] [19] [1]

What deeper 2026 research adds

An August 2026 preprint by Louis Yiven Zhu analyzes 21,024 posted-price observations across 3,208 models and 86 providers. Its estimated annual quality-adjusted decline is 0.73 log points, compared with 0.10 under matched-model measurement. These are log changes, not percentage reductions, and the estimates depend on the benchmark-based quality model. [46]

The paper also reports substantial sensitivity to removing potentially contaminated benchmark anchors. Its task-price estimate is imprecise and does not establish the sign of the change in cost per completed task. We treat it as emerging research rather than settled evidence that every customer’s cost is falling or rising. [46]

The practical implication is to report two separate series: current token prices for a fixed workload, and measured cost per accepted output for a stable task set. Archive each edition before updating it. Benchmark version changes and cohort additions must be marked as breaks in comparability.

The historical animation includes only the three observed quotes above. The other animations change thresholds, workload mix or reasoning effort within one dated snapshot. They should never be captioned as a monthly market trend.

Cite this analysis

Suggested citation: Uvik Software. AI Model Cost Effectiveness Index 2026. Edition 2, data checked 17 October 2026. Benchmark data from Artificial Analysis; API rates from the named providers. Include the edition date when quoting a figure.

Uvik Software AI Model Cost Effectiveness Index

Ready to turn the shortlist into a production decision? Talk to Uvik Software about generative AI consulting.

Sources and calculation files

All sources were accessed on 17 October 2026. URLs identify the source, not a permanent price guarantee. Model configuration pages support individual benchmark rows. The accompanying source register and CSVs make calculations auditable.

• [1] OpenAI API pricing
• [2] Anthropic model and feature pricing
• [3] Google Gemini Developer API pricing
• [4] DeepSeek models and pricing
• [5] Artificial Analysis current model leaderboard
• [6] Artificial Analysis cost and performance methodology
• [7] Artificial Analysis intelligence methodology and versions
• [8] Claude Haiku 5.5 xhigh benchmark
• [9] GPT 6.1 Sol medium benchmark
• [10] GPT 6.1 Sol high benchmark
• [11] GPT 6.1 Sol max benchmark
• [12] Claude Opus 5.5 high with fallback benchmark
• [13] Claude Opus 5.5 xhigh with fallback benchmark
• [14] Claude Opus 5.5 max with fallback benchmark
• [15] Gemini 3.8 Flash high benchmark
• [16] Claude Sonnet 5.5 max with fallback benchmark
• [17] GPT 6 Astra max benchmark
• [18] GPT 4o mini launch pricing on 18 July 2024
• [19] GPT 4.1 family launch pricing on 14 April 2025
• [29] Gemini 4 Argon high benchmark
• [30] GPT 6.1 Sol xhigh benchmark
• [31] GPT 6.1 Sol low benchmark
• [32] GPT 6 Luna max benchmark
• [33] GPT 6 Luna xhigh benchmark
• [34] Claude Haiku 5.5 max benchmark
• [35] Claude Haiku 5.5 high benchmark
• [36] Claude Haiku 5.5 medium benchmark
• [37] DeepSeek V4.1 Flash max benchmark
• [38] DeepSeek V4 Pro 0813 max benchmark
• [39] Kimi K3 max benchmark
• [40] GLM 5.3 max benchmark
• [41] Grok 4.7 high benchmark
• [42] Claude Sonnet 5.5 medium with fallback benchmark
• [43] Claude Sonnet 5.5 high with fallback benchmark
• [44] Claude Sonnet 5.5 xhigh with fallback benchmark
• [45] Claude Opus 5.5 medium with fallback benchmark
• [46] Zhu 2026 The Price of Intelligence preprint
• [47] Anthropic prompt caching mechanics and prices
• [48] Anthropic batch processing documentation
• [49] Google public availability announcement for Gemini 4 Argon

Download the AI Model Cost Effectiveness Index 2026 dataset.