# BigModel vs supermemory

BigModel records 5,700,686 monthly visits with -17% month-over-month growth and Freemium pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.

| Metric | [BigModel](https://openaitools.app/tool/bigmodel) | [supermemory](https://openaitools.app/tool/supermemory-1) |
| :--- | :--- | :--- |
| Website URL | https://bigmodel.cn | https://supermemory.ai |
| Categories | Large Language Models (LLMs), AI API, AI Models, AI Agent | AI API, Large Language Models (LLMs), AI Developer Tools, AI Agent |
| Pricing Model | Freemium | Contact for Pricing |
| Monthly Visits | 5,700,686 | 156,579 |
| MoM Growth Rate | -17% | -7.7% |
| Primary Market | China | United States |
| Direct Visits | 4,230,503 | 71,163 |
| Review Score | 4.6 | 4.5 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## BigModel

BigModel is Zhipu AI's developer-facing platform, offering API access to the company's GLM series of large language models and a broad set of multimodal capabilities. Instead of juggling separate vendors, teams can call text generation, vision-language understanding, OCR, speech recognition, speech synthesis, image generation, and video generation endpoints from one account and one set of credentials.

Beyond raw model access, the platform targets application builders. It includes an agent framework, MCP service support, knowledge-base tooling for retrieval-augmented generation, model fine-tuning workflows, and a model playground for quick testing in the browser. There are also adjacent offerings such as an AI coding assistant and visual/low-code building experiences aimed at teams that want to ship without heavy ML engineering.

For organizations with stricter requirements, BigModel supports private instance deployment and on-premise options, alongside token-based pay-as-you-go and enterprise contracts. This makes it a fit for both individual developers prototyping on a budget and enterprises that need data to stay inside their own infrastructure.

**Pricing & Plans:** BigModel follows a freemium, token-based model: new users typically receive free trial credits, then pay-as-you-go for API usage by tokens consumed. Larger volumes, private instance deployment, and on-premise installations are arranged through contact-for-pricing enterprise agreements. Always confirm current rates on the official pricing page.

- **GLM text and multimodal APIs:** Unified access to GLM large language models plus vision, OCR, speech, image, and video endpoints through one account.
- **Agent and MCP support:** Build tool-using agents and expose or consume MCP services to connect models to external systems.
- **Knowledge base and RAG tooling:** Create managed knowledge bases and retrieval pipelines so models can answer with your own documents.
- **Fine-tuning and playground:** Tune models on your data and prototype prompts interactively in the browser before shipping.
- **Private and on-premise deployment:** Run models in a private instance or on your own hardware for data-residency and compliance needs.

**Strengths:** Broad model coverage, from LLMs to vision, speech, and video, under one platform; Officially backs the GLM family, so model documentation and updates are first-party; Supports private and on-premise deployment for strict data requirements; Includes agent, knowledge-base, and fine-tuning tooling beyond raw API access

**Limitations:** Pricing and documentation are primarily Chinese-first, which can slow English-only teams; Some enterprise capabilities require sales contact rather than transparent self-serve pricing; Token costs can grow quickly for high-volume multimodal workloads

[Profile (Markdown)](https://openaitools.app/tool/bigmodel.md)

## supermemory

Most LLM applications treat memory as an afterthought: chat history is stuffed into the prompt until the window fills up, and everything is lost on the next session or the next model switch. supermemory attacks that problem as infrastructure. Its learner-1 model reads incoming material — conversations, files, URLs, raw data — and performs update, merge, infer and forget operations against a vector-graph database, so learnings stay dense, interconnected and time-aware rather than piling up as an ever-growing transcript.

At request time the relevant memories are injected as tokens directly into the agent's context, which keeps the approach model-agnostic and harness-agnostic. Because learning and memory live outside the model providers, the same accumulated understanding travels with an application whether it runs on Claude, GPT-family models, Gemini or open-weight models, and whether it is driven by an SDK, a plugin or MCP.

The company positions this as the hard part of agent infrastructure and backs it with published numbers: sub-second recall on production traffic, roughly a trillion tokens processed per month, benchmarks on LongMemEval, LoCoMo and ConvoMem, and cost comparisons against markdown-file memory at large corpora. Enterprise features include SOC 2 Type II certification, a HIPAA BAA on higher tiers, GDPR compliance and deployment options ranging from cloud to VPC, on-premises and air-gapped environments.

**Pricing & Plans:** Pricing is split between self-serve tiers and enterprise contracts, which is why the site lists a contact-for-pricing model. Free, Pro and Max tiers cover standard PDF extraction and personal use, while Scale and Enterprise add advanced document extraction with OCR and figure/diagram descriptions, higher limits, security guarantees such as SOC 2 Type II and HIPAA BAA, and deployment choices including your VPC, your data center or air-gapped setups. Exact figures should be confirmed on the pricing page for current rates.

- **learner-1 memory model:** A dedicated model extracts, merges, infers and forgets across stored context so the memory graph keeps improving instead of simply accumulating.
- **Vector-graph memory store:** Learnings link to the entities, sources and moments around them, storing not just content but a time-aware map of how facts relate.
- **Model, harness and MCP agnostic:** Memory lives outside model providers and is used through APIs, SDKs, plugins and MCP, so context carries across models and tools.
- **Real-time context injection:** Relevant memories are injected as tokens into the prompt at request time, giving agents relevant context without unbounded transcripts.
- **Enterprise deployment and compliance:** SOC 2 Type II, HIPAA BAA on higher tiers, GDPR compliance, plus deployment in the cloud, your VPC, your data center or air-gapped.

**Strengths:** Memory is decoupled from model providers, so accumulated context survives model swaps; Rich engineering evidence, including published benchmarks, latency figures and cost comparisons; Multiple integration surfaces — REST API, SDKs, plugins and MCP; Strong enterprise posture with SOC 2 Type II, HIPAA BAA, GDPR and air-gapped deployment options

**Limitations:** Self-serve pricing on the public site is thin, and exact tier costs may require a sales conversation; It is infrastructure rather than an end-user app, so there is real integration work before you see value; Benchmark results and adoption figures are vendor-reported and worth validating against your own workload

[Profile (Markdown)](https://openaitools.app/tool/supermemory-1.md)

## Frequently Asked Questions

### How do BigModel and supermemory compare in monthly traffic?

BigModel has 5,700,686 monthly visits, while supermemory has 156,579. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

BigModel has a month-over-month growth rate of -17%; supermemory has -7.7%.

### How does pricing compare?

BigModel: Freemium. BigModel follows a freemium, token-based model: new users typically receive free trial credits, then pay-as-you-go for API usage by tokens consumed. Larger volumes, private instance deployment, and on-premise installations are arranged through contact-for-pricing enterprise agreements. Always confirm current rates on the official pricing page. supermemory: Contact for Pricing. Pricing is split between self-serve tiers and enterprise contracts, which is why the site lists a contact-for-pricing model. Free, Pro and Max tiers cover standard PDF extraction and personal use, while Scale and Enterprise add advanced document extraction with OCR and figure/diagram descriptions, higher limits, security guarantees such as SOC 2 Type II and HIPAA BAA, and deployment choices including your VPC, your data center or air-gapped setups. Exact figures should be confirmed on the pricing page for current rates.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [DeepSeek](https://openaitools.app/tool/deepseek) | 372,761,591 | +4.3% | Freemium | Delivers exceptional open-weight reasoning performance at very low cost, but lacks BigModel's broad enterprise multi-modal agent building suite. | [.md](https://openaitools.app/tool/deepseek.md) |
| 2 | [OpenAI Platform](https://openaitools.app/tool/openai) | 234,678,639 | -9.6% | Paid | Provides industry-leading frontier intelligence and developer ecosystems, but lacks BigModel's specialized GLM architecture and native Chinese language optimizations. | [.md](https://openaitools.app/tool/openai.md) |
| 3 | [Google AI Studio](https://openaitools.app/tool/google-ai-studio) | 112,620,020 | +2% | Freemium | Offers massive context windows and robust multimodal Gemini APIs, but differs in model architecture and regional enterprise tooling. | [.md](https://openaitools.app/tool/google-ai-studio.md) |
| 4 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Aggregates hundreds of global models under one API endpoint, whereas BigModel provides direct native infrastructure for Zhipu's GLM models. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 5 | [fal.ai](https://openaitools.app/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | One API for 1,500+ production-ready generative media models | [.md](https://openaitools.app/tool/fal-ai.md) |
| 6 | [Composio](https://openaitools.app/tool/composio) | 2,782,603 | +22.8% | Freemium, Contact for Pricing | Focuses on tool integrations and authentication for agents, complementing memory engines rather than storing cross-session context. | [.md](https://openaitools.app/tool/composio.md) |
| 7 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Specializes in serverless open-source model hosting and dedicated GPU clusters, but provides less native proprietary GLM model integration. | [.md](https://openaitools.app/tool/together-ai.md) |
| 8 | [Replicate AI](https://openaitools.app/tool/replicate-ai) | 904,038 | -8.4% | Paid | Run, fine-tune, and deploy machine learning models with one line of code | [.md](https://openaitools.app/tool/replicate-ai.md) |
| 9 | [Fireworks](https://openaitools.app/tool/fireworks-ai) | 659,687 | +6% | Contact for Pricing | Delivers ultra-fast inference speeds for open-weight models, but focuses less on proprietary enterprise agent building toolkits than BigModel. | [.md](https://openaitools.app/tool/fireworks-ai.md) |
| 10 | [LiteLLM](https://openaitools.app/tool/litellm) | 576,967 | +7.9% | Freemium, Contact for Pricing | One open-source AI gateway for 100+ LLM providers | [.md](https://openaitools.app/tool/litellm.md) |

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Source: https://openaitools.app/compare/bigmodel-vs-supermemory-1

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