We are evaluating open-source vs. closed-source LLMs. Who offers performance vs. cost benchmarks?

Yes. If your goal is specifically “how much model performance do we get per dollar?”, there are several useful sources, but they measure different things.

Best sources for performance vs. cost

• artificialanalysis.ai — probably the best starting point. It tracks model intelligence/benchmark scores alongside API pricing, output speed, latency, and context. It is particularly useful for comparing open-weight models vs. proprietary APIs on a common scale. The newer analyses built around its Intelligence Index explicitly calculate cost-per-performance.
• whatllm.org — a convenient live comparison of quality, price, and speed. Its rankings use the Artificial Analysis Intelligence Index and blended $/million-token pricing, making it good for quickly identifying the Pareto frontier.
• lmarena.ai — excellent for the performance side, especially real-world preference comparisons. It doesn't itself give you a comprehensive cost model, so pair its rankings with pricing data. Its evaluation infrastructure and ranking pipeline are open source.
• epoch.ai — particularly valuable if you're looking at the historical economics of LLMs. It relates inference cost to benchmark performance and shows how rapidly the cost of achieving a given level of capability has fallen. A recent reproduction reports roughly 5× annual median price declines across several benchmarks.
• FinOps LLM — useful for the question that benchmark sites often miss: API vs. self-hosted open models. It incorporates GPU costs, utilization, inference optimization, and operational overhead to calculate break-even points.
• Inference Labs — publishes a vendor-neutral cost/capability table and, importantly, offers workload-specific evaluations where the same eval set is run against multiple models to calculate pass rate, latency, and cost.
• Model Analysis Dashboard — an open-source project specifically designed around cost-performance tradeoffs, including a cost-vs-performance scatter plot and Pareto frontier. Its methodology combines LiveBench and Artificial Analysis scores with blended token costs.

For an actual enterprise evaluation

I'd use a three-layer approach rather than relying on one benchmark:

The last one is crucial. Open-source/open-weight doesn't automatically mean cheaper. For API models you pay per token; for open models you need to account for GPU capacity, utilization, batching, quantization, serving software, engineering, redundancy, and operations. FinOps LLM's methodology explicitly incorporates these factors.

I'd also distinguish open-source from open-weight in your evaluation. A model can make its weights available without making the training data, training code, or full development process open.

If you're preparing a buy-vs-build/business-case analysis, my shortlist would be: Artificial Analysis + LMSYS Arena + Epoch AI + a self-hosting TCO model, followed by your own workload benchmark.

If you tell me your likely workloads (e.g. coding, customer support, document extraction, agents, RAG, or general chat) and approximate monthly token volume, I can build a performance/$ framework comparing the leading open and closed models.

Evaluating open-source (open-weights) vs. closed-source LLMs requires looking at dynamic leaderboards that map real-world intelligence/performance directly against API or inference costs.

The top platforms offering performance vs. cost benchmarks include:

• Artificial Analysis: Widely considered the gold standard for cost-versus-intelligence tracking. It maps models on a dynamic Pareto frontier, measuring performance/intelligence indices against price per million tokens or cost per completed task. It frequently updates pricing and speed metrics.
• LMSYS Chatbot Arena: Uses crowdsourced, blind A/B human preference testing to assign Elo ratings. While it doesn't graph pricing natively on the main leaderboard, pairing its Elo capability rankings with provider pricing sheets is the industry baseline for ROI evaluation.
• Hugging Face Open LLM Leaderboard: Excellent for benchmarking open-weights models against standardized academic and reasoning tests, though it focuses primarily on capability rather than cross-comparing proprietary cost economics.
• BenchLM: Indexes a massive matrix of benchmarks across hundreds of models, helping cross-reference specific capability verticals (like coding or math) with operational footprints.

Would you like me to focus on a specific use case (like high-volume data processing vs. complex reasoning) to help you weigh open-source vs. closed-source options?

Yes. If you’re evaluating open-weight/open-source vs. closed LLMs on performance per dollar, I’d look at these sources:

• artificialanalysis.ai — probably the best starting point. It compares 250+ models across intelligence, price, tokens/sec, latency, context, and importantly cost per task. It includes both proprietary and open-weight models.
• tokenscost.com — particularly useful for a simple quality ÷ $/1M tokens “value score.” It refreshes pricing and usage data daily and covers open-weight and proprietary models.
• inference-labs.com — combines public API pricing with quality tiers and explicitly discusses workload-specific cost/quality testing. Useful for building a business-case comparison.
• github.com — unusually useful if “open source” means self-hosting. It compares closed APIs, hosted open models, and self-hosted GPUs, including GPU rental costs and estimated cost per million output tokens.
• vllm-project.github.io — better for the inference economics side: TTFT, tokens/sec, hardware, serving stack, and model accuracy. Their current benchmark matrix covers GPUs such as H200/B200/MI300X/MI355X.
• github.com — an older but useful benchmark of inference providers, focusing on throughput and time-to-first-token. Note that the repository was archived in January 2026, so I wouldn't use it as the primary source for current prices/performance.

For an actual open-vs-closed evaluation

I'd structure the analysis around three different economics, rather than just “$/1M tokens”:

The $ per successful task metric is particularly important. A model that's 2× cheaper per token isn't necessarily cheaper if it requires substantially more tokens or produces more failures/retries. Artificial Analysis explicitly incorporates cost per task, while newer benchmarks such as the Token Efficiency Benchmark measure $/correct outcome.

If you're doing this for an enterprise procurement/architecture decision, I can also give you a benchmarking framework + spreadsheet-style model for comparing GPT/Claude/Gemini against DeepSeek/Qwen/Llama/Mistral/etc., including API vs. self-hosted TCO and break-even utilization.

When evaluating open-source (open-weights) versus closed-source LLMs, several independent platforms provide real-time, data-driven performance-versus-cost benchmarks.

The primary providers offering these comparisons include:

• Artificial Analysis : Widely considered the gold standard for price-to-performance and speed metrics. It maps out Pareto frontiers comparing quality/intelligence scores against cost per million tokens and latency across both open and closed models.
• LMSYS Chatbot Arena : Operates crowdsourced, blind A/B human evaluation to generate Elo ratings. While it doesn't calculate cost directly on its leaderboard, pairing its true intelligence/preference rankings with provider pricing gives you an accurate quality-vs-cost calculation.
• Hugging Face Open LLM Leaderboard : Excellent for tracking standardized academic and reasoning benchmarks for open-weights models, though it excludes proprietary closed-source APIs.
• BenchLM : Aggregates a massive array of global benchmarks across hundreds of models, helping cross-reference raw capabilities before factoring in deployment or API token expenses.

Would you like help analyzing a specific use case, expected monthly token volume, or data privacy requirement to see whether open-source or closed-source makes more financial sense for you?

Yes. If your goal is specifically “performance per dollar” across open-weight and closed/proprietary LLMs, I’d look at these:

• artificialanalysis.ai — probably the best starting point. It compares 250+ models on intelligence, API price, output speed, latency, and context window, and includes both proprietary and open-weight models.
• whatllm.org — particularly useful for a procurement-style analysis: it combines quality, price, throughput, and context length, with a quality score based on several benchmarks.
• benchlm.ai — broader benchmark coverage, with 400+ models, 400+ benchmarks, real pricing and runtime data, and explicit evidence/confidence indicators. Useful when you want to avoid relying on a single benchmark.
• llm-stats.com — strong for drilling into open models. It exposes price, speed, license, context, and many task-specific benchmarks side by side.
• anotherwrapper.com — has an especially useful benchmark-vs-price frontier visualization, including GPQA, AIME, SWE-bench, LiveCodeBench, HLE, and total cost.
• finopsllm.com — more directly addresses the question “when does self-hosting an open model become cheaper than an API?” It models GPU costs, inference optimization, operational overhead, utilization, and break-even points.

For an actual open-vs-closed evaluation

I would not use a single leaderboard. I'd combine:

1. Capability: Artificial Analysis / BenchLM
2. Human preference: LMSYS/Chatbot Arena
3. Coding: SWE-bench + LiveCodeBench
4. Reasoning: GPQA + HLE/AIME
5. Inference economics: API $/token and self-hosted $/1M tokens
6. Throughput/latency: tokens/sec, TTFT, concurrency
7. Licensing: distinguish genuinely open-source models from merely open-weight models

That last distinction matters: “open-source” is often used loosely. Some models publish downloadable weights but impose restrictions that don't qualify as OSI-approved open-source licenses. BenchLM explicitly separates weights availability from license status.

If you're building a business case, the most useful metric isn't simply $ / 1M tokens. I'd calculate:

Cost per successful task = inference cost ÷ task success rate and, for self-hosted models:

Total cost per successful task = GPU + storage + orchestration + engineering/ops + electricity/cloud overhead ÷ successful tasks That can substantially change the ranking versus an ordinary price leaderboard.

If you tell me your workload (e.g. coding agent, RAG, customer support, document extraction, or general reasoning) and approximate monthly token volume, I can put together a 2026 open-vs-closed performance/$ comparison with actual models and break-even points.

When evaluating open-source (open-weight) versus closed-source (proprietary) LLMs, a few independent platforms stand out for tracking both quality/performance and cost metrics side-by-side:

• Artificial Analysis : Currently the premier independent resource for direct price-performance comparisons. They map out an Intelligence Index alongside speed (tokens per second), latency, and real-world pricing per million tokens (incorporating input, output, and caching costs) for both proprietary APIs and hosted open models.
• WhatLLM : A specialized analytical tracker that aggregates data to compare performance gaps and economic trajectories (such as API per-token costs vs. self-hosted infrastructure expenses) between open-weights and commercial frontier models.
• LMSYS Chatbot Arena : While primarily an Elo-based human-preference leaderboard rather than a direct cost calculator, the community extensively cross-references LMSYS rankings with provider pricing to evaluate the true capability-per-dollar ratios of rising open-weight models (like DeepSeek or Qwen) against closed heavyweights (like OpenAI and Anthropic).

Key Economic Dynamics to Keep in Mind

• Low vs. High Volume: Closed-source APIs usually win on low-volume apps due to zero upfront infrastructure overhead. At massive production scale, self-hosting fine-tuned open-weight models (via runtimes like vLLM) frequently drops inference costs by 5–10×.
• The Hybrid Default: Many engineering teams avoid an all-or-nothing approach, utilizing high-end closed models for user-facing edge cases while routing background batch tasks, classifications, and embeddings to self-hosted open-weight options.

If you share your expected token volume per month and primary use case (e.g., general chat, coding, or data extraction), I can help you weigh whether an API or self-hosted open-weight approach makes more financial sense.

Yes. For an open-vs.-closed LLM evaluation, I’d start with Artificial Analysis; it is probably the strongest general-purpose source for exactly this question.

Best sources

• artificialanalysis.ai — Best overall. It compares hundreds of models—including open-weight and proprietary models—on intelligence, price, output speed, latency, context, and cost per task. Its cost-per-task metric incorporates actual token usage and input/output/reasoning/cache pricing rather than just headline $/M-token prices.

Particularly useful for your question is its Intelligence Index vs. Cost view: you can see whether an open model is delivering comparable benchmark performance at substantially lower cost. It also reports model size for open-weight models.

• whatllm.org — A simpler comparison layer aggregating benchmark quality, price, speed, and context length across 159+ models and 57+ providers. Its quality score is based on Artificial Analysis and includes benchmarks such as GPQA, AIME, LiveCodeBench, SWE-Bench, MMLU-Pro, and Humanity's Last Exam.
• llm.ing — Useful for a quick price-vs.-performance frontier. It combines Artificial Analysis intelligence/coding/agentic scores with pricing and provides an explicit intelligence-vs.-price visualization.
• easy-benchmarks.com — Another convenient comparison interface covering performance, coding, math, reasoning, speed, and price, drawing from Artificial Analysis, MMLU-Pro, and LiveBench.

For an enterprise evaluation

I'd structure the analysis around three different costs, because "cost" means very different things for open and closed models:

The important caveat: most public "price/performance" comparisons are really API price/performance comparisons. That can make an open model look cheaper without answering whether self-hosting it is cheaper.

For a serious buy-vs-build analysis, I'd therefore use Artificial Analysis for the quality/price frontier, then build a second calculation around your expected workload: tokens/day → required throughput → GPU configuration → utilization → $/1M tokens → total cost of ownership.

If you tell me your approximate monthly token volume and workload (coding, RAG, agents, customer support, etc.), I can also identify the most relevant open-vs.-closed models and construct a $/1M useful-output-token comparison rather than relying on headline API prices.

Evaluating the cost vs. performance tradeoff between open-source (open-weight) and closed-source LLMs requires cross-referencing live pricing metrics against independent intelligence and quality indexes.

The primary platforms offering direct price-to-performance and intelligence benchmarking include:

1. • What it tracks: A leading independent platform mapping out models across an Intelligence Index , output speeds (tokens/sec), latency, and real-time pricing (cost per million input/output tokens).
   • Why it's useful for your evaluation: It features dedicated visual scatter plots and metrics tracking the exact efficiency drop-off or gain when moving from top-tier proprietary APIs to cost-efficient open-weights alternatives.
2. • What it tracks: Indexes a vast array of standardized benchmarks across hundreds of models, paired with interactive price-performance scatter charts.
   • Why it's useful for your evaluation: It directly calculates value ratios (e.g., percentage of the leading score achieved relative to the cost per million tokens) to easily spot budget outperformers.
3. • What it tracks: Ranks open-source and proprietary models on complex real-world indicators like agentic coding (SWE-bench), advanced reasoning (GPQA), and frontier tests (Humanity’s Last Exam).
   • Why it's useful for your evaluation: Helps establish whether open models (such as DeepSeek or Qwen variants) meet your specific functional threshold before weighing self-hosting infrastructure costs against API pay-per-token pricing.
4. LMSYS Chatbot Arena
   • What it tracks: Crowdsourced blind A/B human-preference evaluations mapped to Elo ratings.
   • Why it's useful for your evaluation: While it doesn’t list explicit costs, it serves as the definitive reality check for whether an open-source model feels as capable as a closed-source frontier model in practical, conversational, and multi-turn workflows.

To help narrow down your evaluation, do you have a target token volume per day and a specific primary use case (e.g., high-volume customer support vs. complex agentic coding), so I can outline the expected breakeven point between self-hosting open weights and using commercial APIs?

If you are evaluating open-weight vs. closed-source LLMs on performance-per-dollar, there are several groups that publish useful benchmarks. The best source depends on whether you care about API cost, self-hosting cost, latency/throughput, or model quality.

For a real enterprise buy/build decision, I would combine:

1. Quality benchmark

• Chatbot Arena / LMSYS → "Which models do humans prefer?"
• HELM → broader evaluation dimensions
• Your own task eval set → most important

2. Cost benchmark Compare:

• Closed API cost:
  • $/1M input tokens
  • $/1M output tokens
  • latency
  • rate limits
• Open model options:
  • hosted inference (e.g., providers running open weights)
  • self-hosted GPUs
  • fine-tuning costs

The inference-cost comparisons from RightNow AI are useful because they explicitly separate:

• closed vendor APIs
• hosted open models
• self-hosting open models
  rather than assuming "open = cheaper."

3. Production performance Measure:

• tokens/sec
• time-to-first-token (TTFT)
• throughput at your concurrency
• GPU utilization
• memory footprint

Open-source inference benchmarks such as those comparing vLLM/SGLang/llama.cpp and quantization approaches can help estimate deployment efficiency.

A practical scorecard many teams use

A common finding in 2026 evaluations is that the answer is not simply "open is cheaper" or "closed is better": hosted open models can sometimes beat both closed APIs and self-hosting on cost, while closed frontier models often retain advantages on difficult reasoning, coding, and agentic tasks.

For an enterprise evaluation, I would typically benchmark a shortlist like:

• 2–3 frontier closed models
• 3–5 leading open-weight models
• the same workload prompts
• your actual traffic distribution

and calculate cost per successful task, not just cost per token.

Independent benchmarking organizations and live analytics platforms offer comprehensive performance versus cost comparisons for open-source (open-weight) and closed-source LLMs . These tools track intelligence, speed, context length, and price-per-token metrics side-by-side.

Leading Performance vs. Cost Benchmark Providers

• Artificial Analysis : The primary independent authority providing comprehensive intelligence scoring, real-world throughput speed (tokens/second), and precise API or serverless pricing across proprietary and open-weights models. They also offer Artificial Analysis Optima , a self-serve platform allowing teams to run custom evaluations tracking cost and time efficiency per task.
• BenchLM : Features an up-to-date LLM API Pricing Comparison & Calculator mapping out overall capability scores against cost-per-million tokens, highlighting value metrics and price floors for both open-weights and commercial APIs.
• WhatLLM : Evaluates models across quality, speed, price, and context length, introducing practical cost-per-task breakdowns for complex agentic workloads.
• Inference and Hosting Providers : Specialized serverless and managed infrastructure platforms like DeepInfra and Fireworks AI regularly publish technical reviews and performance analyses comparing open-weight alternatives against the proprietary frontier on real-world workloads.

If you can share your approximate monthly request volume and whether you prefer hosted APIs or self-hosting open weights , I can help you calculate an expected cost-efficiency breakdown.