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27 leading models · 15+ data fields · scenario picks, speed & latency, function-calling, bilingual ability and enterprise features. Sort, filter, and compare up to 4 models side by side. Click a row to open the full profile.
Switch dimensions to rank models by capability area, use filters and sorting, click a rank badge to add to comparison, and click a row to open the full profile.
Recommendations for different buyer profiles, based on overall score, price, speed and scenario fit.
It tops all three dimensions at an overall 95.0, eight points clear of the runner-up. It is also the priciest ($10/$50) and relatively slow (~30 tok/s), best suited to deep-reasoning workloads where quality outweighs cost.
DeepSeek-V4-Pro ranks 8th overall at just $0.44/$1.32, roughly 30× the price-performance of Claude Fable 5. Open weights with self-hosting, strong math reasoning and an 82 function-calling score — ideal for budget-constrained production.
Doubao Seed 2.1 Pro ranks first globally in video understanding at 89.2, and Qwen3.8-Max offers unmatched multimodal value. Chinese-Mainland models, however, generally trail international models by 15–20 points on English — evaluate carefully for overseas deployments.
Tell us your needs and generate a tailored selection report automatically from 15+ data fields across 27 models.
Model selection, cost modeling and an implementation roadmap across three scenarios: customer support, internal knowledge Q&A and coding assistance
Use Doubao Seed 2.1 Pro as the workhorse (support + Q&A; strong Chinese, low cost, self-hostable), Claude Sonnet 5 dedicated to coding, and DeepSeek-V4-Pro as the open-weight backup. Route by scenario through one API gateway to avoid lock-in.
A 500-person technology company deploying an AI-assistant platform within 6 months across three core scenarios:
Eight models are shortlisted from 22 by "overall ≥ 70 + Chinese support + commercial API available":
Weighted by each scenario's KPI weights (out of 100):
Estimated at 1,500 input + 500 output tokens per call and $1 = ¥7.2; open-weight plans include GPU depreciation. Enterprise volume purchases typically earn 15–30% discounts.
Keep sensitive data on self-hosted Chinese-Mainland models; use international models only for desensitized code, with classified routing at the API gateway.
Multi-model combo plus a gateway abstraction layer; decouple prompts from models and re-evaluate alternatives every six months.
Build a regression set (≥50 cases per scenario), pin the API version and retain the previous version for 3 months.
Use mature frameworks such as vLLM/TGI or buy the vendor's enterprise deployment service, with one inference engineer.
Set daily budget caps and alerts, use layered summarization for long documents, and sign an annual framework agreement to lock unit prices.
RAG across scenarios, state when no source exists, route high-risk questions to humans, and sample 100 conversations weekly.
API-gateway prototype, standard test set, self-hosting PoC, cost measurement, Go/No-Go review
10% canary on one business line, knowledge-base RAG (500 docs), IDE plugin, monitoring dashboard
Support at full scale, Q&A for 500 staff, coding for 100 developers, 3 training sessions
Smart-routing and cost optimization, fine-tuning review, new-scenario planning, annual review
Aggregated from public information online (public benchmarks including LMArena, Artificial Analysis, SuperCLUE, BFCL and SWE-bench, plus official vendor documentation) and independently weighted. This is an independent third-party analysis; model names and trademarks belong to their owners, and the content is for reference only, not procurement or investment advice.
The more complete the information, the more precise the report. Fields marked * are required.
| Plan | Daily calls | Est. annual API cost | Avg cost/call |
|---|
The above is the free summary. The full report has 8 chapters with in-depth comparison, 3-year TCO, six risk mitigations and a 6-month roadmap, ready for internal decision meetings.
Models are shortlisted from 22 by overall ability, scenario fit and compliance; the full process and reasons for exclusion are in the complete comparison table.
Shortlisted models are weighted by each scenario's KPI weights, with Top-5 fit bars and selection analysis for every scenario.
Data comes from public benchmarks including LMArena, Artificial Analysis, SuperCLUE, BFCL and SWE-bench and from official vendor documentation, independently weighted. The full weighting methodology is on the Terms page. This is an independent third-party analysis and is not procurement or investment advice.
Structured data API for 27 leading LLMs: ranks and scores, pricing, speed, context, function-calling and English/Chinese ability. Integrate once and stay in sync.
Every core dimension for model selection, returned as structured JSON — no scraping or cleaning required
RESTful design, GET requests, JSON responses, API-key authentication
Free tier for development and testing; upgrade for production; Enterprise is customizable
Integrate in a few lines; below is an example fetching the overall board
From sign-up to first call in 5 minutes
Get a key and the free tier activates immediately; Enterprise customers can book a technical demo and data sample
Hard-constraint filtering plus soft-weighted matching across 15+ dimensions for 27 models, returning a Top 3 in about 60 seconds. Free to use.
Modelspectra collects, cleans, standardizes and independently weights publicly available model benchmarks, official vendor documentation and public leaderboards to provide structured information on large-model rankings, capability dimensions, pricing, speed, context length, function-calling, English/Chinese ability and enterprise features, along with recommendation, comparison, questionnaire-matching and report-generation tools.
The Service is provided "as is." Evaluation results are independent analytical views based on public data; they do not represent the official position of any model vendor and are not investment, procurement or technical-decision advice.
You are responsible for all activity under your account and warrant that registration information is true and accurate. Scraping, quota circumvention and reverse engineering to abuse the Service are prohibited.
All scores are aggregated from public information online, standardized and independently weighted as follows. Sub-scores are first normalized to 0–100 per benchmark convention and then weighted and aggregated; boards update monthly, with immediate additions on major releases.
| Board | Constituent benchmarks and weights |
|---|---|
| Overall | LMArena human blind-test ELO 40% + Artificial Analysis composite 40% + SuperCLUE Chinese ability 20% |
| Coding | SWE-bench 35% + HumanEval+/LiveCodeBench 25% + Terminal-Bench 20% + LiveCodeBench composite 20% |
| Multimodal | MMMU 40% + video-understanding benchmark 30% + AI2D diagram understanding 30% |
Auxiliary dimensions (tokens/s, TTFT, context effectiveness, BFCL function-calling, English/Chinese ability and refusal rate) come from the corresponding public benchmarks or vendor documentation. Models within 3 points show no statistically significant difference and may trade places on specific tasks. The weighting scheme may evolve with the benchmarks; changes are dated on this page.
We have reviewed each source's license; details follow:
| Source | Use here | License | Commercial status |
|---|---|---|---|
| LMSYS Chatbot Arena | Overall ELO score | Public-leaderboard citation; raw battle data CC BY-NC 4.0 | ✓ Public scores citable with attribution |
| Artificial Analysis | Overall score, speed/TTFT, context effectiveness | Site terms limit to personal, noncommercial use | ⚠ Commercial license pending |
| SuperCLUE | Overall Chinese-ability score | Open on GitHub, attribution required | ✓ Commercial use with attribution |
| BFCL (Berkeley) | Function-calling score | Apache 2.0 | ✓ Fully commercial |
| SWE-bench / LiveCodeBench / HumanEval+ | Coding benchmarks | MIT / Apache 2.0 | ✓ Fully commercial |
| MMMU / AI2D / video benchmarks | Multimodal benchmarks | Apache 2.0 / CC BY 4.0 | ✓ Commercial use with attribution |
| Official vendor documentation | Pricing, params, context, enterprise features | Public factual information, descriptive citation | ✓ Factual data citable |
Evaluation scores, prices and parameters are objective factual data not themselves protected by copyright; however, the Service's selection, standardization, weighted arrangement, visualization, copy and interface design, and the database as a compiled whole, are owned by the Service and protected by copyright and database law.
API keys are for you/your organization only and may not be shared or resold. Exceeding quota results in rate-limiting or suspension. We reserve the right to adjust fields, quotas and pricing, giving paid users 30 days' notice of material changes.
Enterprise reports are automatically generated from public data after you enter your needs. The summary is free; the full report is a one-time paid digital product (currently ¥99). Preview the full structure via the sample template before paying. As digital content cannot be returned once delivered, reports are generally non-refundable once generated and unlocked; we regenerate for free if there is a clear generation error. Cost estimates rely on assumed parameters and are for reference only; vendor quotes prevail.
Model names and trademarks such as GPT, Claude, Gemini, Qwen, Doubao, DeepSeek, Kimi, GLM, MiniMax and ERNIE belong to their respective owners (OpenAI, Anthropic, Google, Alibaba, ByteDance, DeepSeek and others) and are used only as descriptive, nominative fair use. The Service has no affiliation, sponsorship, endorsement or investment relationship with any of them.
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We may update these Terms as the business and applicable laws require, dating changes on this page; continued use constitutes acceptance. These Terms are interpreted and disputes resolved under the laws of the People's Republic of China (users in other jurisdictions also comply with their local mandatory law). Questions may be sent to Contact@modelspectra.com .
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Modelspectra is an independent intelligence platform for AI models. We bring together global public evaluations, benchmark data and vendor information and recalibrate it with a unified, transparent, reproducible method — helping researchers, developers and enterprise decision-makers see clearly and choose well in a fast-moving model landscape.
We take no vendor funding and allow no ranking interference; every conclusion comes from public data and independent weighting, at a neutral distance from those evaluated.
We publish sources, license status and calculation conventions. Every score traces to an underlying benchmark, with methods and assumptions open to inspection.
Standardized normalization and weighting let scores from different periods and sources be compared and checked on one consistent scale.
Beyond a single total score: ability, price, speed, context, function-calling, English/Chinese performance and enterprise readiness, shown across dimensions.
Names and trademarks are descriptive only. We take no sides and give no endorsements — only evidence and the analysis it supports.
We turn raw benchmarks into actionable selection insight, so individual developers and enterprise teams alike can find the right model fast.
Starting from one aggregated leaderboard, Modelspectra is growing into complete decision infrastructure for models: continuously updated multi-dimensional boards, the interactive ModelMatch finder, automated enterprise selection reports, and a data API for product and research teams. When model ability becomes a general factor of production, the ability to understand and compare models is itself a product worth doing well.
In-depth research and benchmark interpretation: quarterly flagships, capability deep-dives, price-performance analysis and open-weight ecosystem notes. Every report runs on our unified, standardized data pipeline, with traceable, checkable conclusions.
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