If you want the highest possible quality regardless of price, the US-based Claude Opus 5, Claude Fable 5, and GPT-5.6 Sol are still ahead. If price, open weights, and independence from a single cloud matter more, the Chinese GLM-5.2, DeepSeek V4 Pro, and MiniMax M3 offer a much stronger cost structure. And China’s current leader in the overall independent ranking is Kimi K3, not automatically the newest Qwen.
For a user in Russia, there is another layer of choice: a model may be excellent, but the official account or payment method may be unavailable. So below, we compare four factors at once: quality, price, access, and data boundary.
Short answer. According to the Artificial Analysis Intelligence Index v4.1 as of August 9, 2026, the leaders are Claude Opus 5 — 61 points, Claude Fable 5 — 60, GPT-5.6 Sol max — 59, and Kimi K3 — 57. In a typical API scenario with 100,000 input tokens and 20,000 output tokens, Kimi costs about $0.60, while DeepSeek V4 Pro and MiniMax M3 are about $0.061 and $0.054. For Russia, the most reliable path is not a “magic VPN,” but either a legitimate foreign account and card with truthful details, or a ruble-based aggregator, or an open-weight model in your own environment.
Prepared and reviewed on August 9, 2026. Prices, rankings, and regional rules are a snapshot as of that date. No affiliate links. This is not legal or financial advice.
Author and fact-check: the editorial team at AI Dawn. We did not run our own lab benchmark of ten models: the numeric ranking comes from independent Artificial Analysis, prices come from vendor documentation, and the calculations are shown by formula.
Contents
- How we selected the best models
- Overall ranking
- Top 5 Chinese models
- Top 5 American models
- How much the same task costs
- Differences in approach
- Access from Russia
- Virtual cards
- Selection matrix
- FAQ
How we selected the best models
The word “best” is almost useless without a task. We included models that are simultaneously relevant in August 2026, available through API or open weights, and strong performers in the independent Artificial Analysis Intelligence Index. For each model, we used the current index v4.1, not the highest historical result from a promotional release.
The index aggregates reasoning, knowledge, coding, and agentic-task tests. It is a useful general benchmark, but not a guarantee of quality in Russian, in your documents, or in your codebase. As the Stanford AI Index 2026notes, top models are converging, and saturation in popular benchmarks reduces the explanatory power of a single score.
Our four selection layers:
- Quality on your task: 50–200 anonymized examples and a predefined acceptance criterion.
- Total cost of results: tokens, reasoning, retries, tools, cache, and employee time.
- Access and payment: supported country, KYC, card, limits, and ban risk.
- Data boundary: direct API, intermediary, or self-hosted deployment; storage and training terms.
The ranking shows the maximum tested reasoning configuration. In real-world use, a shorter mode may be cheaper and faster, but score lower.
Overall ranking: 5 Chinese and 5 American models
| Group rank | Model | Company country | AA Index v4.1 | Context | API, $ per 1M input/output tokens | Best use case |
|---|---|---|---|---|---|---|
| CN-1 | Kimi K3 | China | 57 | 1.05M | $3 / $15 | complex agents and long documents |
| CN-2 | GLM-5.2 max | China | 51 | 1M | $1.40 / $4.40 | open-weight reasoning and code |
| CN-3 | Qwen3.7-Max | China | 46 | 1M | $2.50 / $7.50 | Alibaba ecosystem and agentic workflow |
| CN-4 | DeepSeek V4 Pro | China | 44 | 1M | $0.435 / $0.87 | lowest price for strong reasoning |
| CN-5 | MiniMax M3 | China | 44 | 1.05 million | $0.30 / $1.20 up to 512K | multimodal capabilities and low-cost API |
| US-1 | Claude Opus 5 | USA | 61 | 1 million | $5 / $25 | maximum overall quality |
| US-2 | Claude Fable 5 | USA | 60 | 1 million | $10 / $50 | tasks where pilot testing confirms Fable’s advantage |
| US-3 | GPT-5.6 Sol max | USA | 59 | 1.05 million | $5 / $30 | code, tools, and the OpenAI API |
| US-4 | Grok 4.5 high | USA | 54 | 500K | $2 / $6 | strong price-to-performance ratio among closed US models |
| US-5 | Gemini 3.1 Pro Preview | USA | 46 | up to 1 million | $2 / $12 up to 200K | Google stack and long context |
Evidence. Scores — Measured: current cards and the Artificial Analysis leaderboard as of 08/09/2026. Prices and context — Measured: public provider pages on the same date. “Best use case” is an editorial Calculated synthesis, which should be verified on your own dataset. For Gemini, requests above 200K cost $4/$18; for MiniMax, context above 512K costs $0.60/$2.40.
Why does Qwen3.7-Max show 46 now, when May publications mention 56.6? The earlier figure referred to the previous v4.0 version of the index. The current Qwen3.7-Max card reflects the revised rating, and the historical result cannot be directly mixed with v4.1. This is a good example of why the date and version matter more than a nice-looking number.
Figure 1. Current AA Index v4.1, scores (Measured, 08/09/2026). One block equals about two points; exact values are shown on the right.
Claude Opus 5 ██████████████████████████████ 61
Claude Fable 5 ██████████████████████████████ 60
GPT-5.6 Sol max █████████████████████████████ 59
Kimi K3 ████████████████████████████ 57
Grok 4.5 high ███████████████████████████ 54
GLM-5.2 max ██████████████████████████ 51
Qwen3.7-Max ███████████████████████ 46
Gemini 3.1 Pro ███████████████████████ 46
DeepSeek V4 Pro ██████████████████████ 44
MiniMax M3 ██████████████████████ 44Top 5 Chinese AI models
1. Kimi K3 — the strongest Chinese result
Kimi K3 scores 57 and trails the overall leader Opus 5 by just four points. According to the independent Kimi K3 card the model supports about 1.05 million tokens of context; the official Kimi pricing is $3 per million input tokens, $15 per million output tokens, and $0.30 for cached input.
This is the choice for long agentic tasks, analysis of large documents, and cases where you need near-frontier performance at a lower cost than the top US models. The downside is expensive output compared with other Chinese models. For chat subscriptions, Kimi lists plans from $19 to $199 per month, but subscription limits cannot be directly compared with API tokens.
2. GLM-5.2 — the best open balance of quality and control
GLM-5.2 max scores 51. Artificial Analysis calls it the leader among open-weight v4.1 models: 1 million context, MIT license, 744 billion total parameters, and 40 billion active at inference. The first API tier is priced at $1.40/$4.40, and cached input is $0.26.
The main advantage is not just price: the weights can be deployed with a compatible cloud provider or in your own environment. That reduces dependence on a single foreign account. The limitation is verbosity: independent testing shows more reasoning tokens, so a low price does not always mean the cheapest completed task.
3. Qwen3.7-Max — Alibaba’s mature agentic model
Qwen3.7-Max scores 46 in the current version of the index. In the international Alibaba Model Studio The price is $2.50/$7.50, and the model documentation indicates a context window of up to 1 million tokens.
Qwen is worth choosing if you already use Alibaba Cloud, need an OpenAI-compatible endpoint, or value the Qwen ecosystem. But this is a proprietary Max model: the existence of open models in the Qwen family does not make this version open.
Why not Qwen3.8-Max? The new model has already appeared in the August information cycle, but as of the cutoff date there is no independent result in the same v4.1 table. So it remains on the watchlist rather than earning an undeserved spot based on the vendor's claim. After a comparable test, the ranking should be revisited.
4. DeepSeek V4 Pro — best budget reasoning model
DeepSeek V4 Pro gets 44, supports 1 million tokens, and is released under the MIT license. The official DeepSeek pricing is $0.435/$0.87, with cached input at about $0.003625 per million tokens.
This is the most obvious candidate for high-volume classification, data extraction, draft code, and multi-step tasks with large output volume. But the savings need to be checked against quality: if a more expensive model solves the task on the first try and DeepSeek takes three tries, the difference closes quickly.
5. MiniMax M3 — a low-cost multimodal alternative
MiniMax M3 also gets 44. An independent model card indicates a context window of about 1.05 million and support for multiple modalities. In the official pay-as-you-go pricing up to 512K, the price is $0.30/$1.20, and long context costs twice as much. There are monthly plans for $20, $50, and $120.
MiniMax is attractive for multimodal prototyping and low-cost agents. But the license is not as permissive as MIT at GLM and DeepSeek: before commercial self-hosting, read the current terms rather than relying on the word open.
Top 5 American AI models
1. Claude Opus 5 — the leader in the overall ranking
Claude Opus 5 scores 61 — the highest result in the dataset. Artificial Analysis ranks it above Fable 5, GPT-5.6 Sol, and Kimi K3. Anthropic's official documentation lists 1 million context and a price of $5/$25.
Opus 5 makes sense for complex analysis, agent workflows, code, and work where the cost of an error is higher than the cost of tokens. At the same time, buying it for every classification task is not rational: a model router can send only the hard 5–15% of requests to Opus, while the rest go to DeepSeek, MiniMax, or Grok.
2. Claude Fable 5 — almost the leader, but not the economic default
Claude Fable 5 gets 60, but Anthropic's official pricing is $10/$50, which is twice as high as Opus 5. Context is 1 million. For some cyber and bio requests, safeguards may route the answer to an earlier model, which also needs to be considered for reproducibility.
Fable should not be chosen just because it is second. It is justified if, on your specific task, it delivers a significantly higher acceptance rate, fewer fixes, or better long autonomous runs. Otherwise, Opus 5 looks stronger economically.
3. GPT-5.6 Sol max — a strong general-purpose API for development
GPT-5.6 Sol max gets 59. The official OpenAI card lists a 1.05 million context window, a 128K max output, pricing of $5/$30, and $0.50 for cached input.
Its strength is a mature ecosystem of tools, structured output, and integrations. For teams already using an OpenAI stack, the migration cost may be lower than the pricing difference. For Russian users, the main drawback is not the model but the unsupported geography of the official service.
4. Grok 4.5 high — the best US price/performance tradeoff
Grok 4.5 high gets 54. xAI and API documentation list a 500K context window and pricing of $2/$6.
By the overall index, Grok beats all Chinese models except Kimi K3, but the typical API bill is noticeably lower than the American top three. This is a strong candidate for the primary closed-model route if the xAI ecosystem, account region, and data policy work for you.
5. Gemini 3.1 Pro Preview — the choice for the Google stack
Gemini 3.1 Pro Preview gets 46 in the current independent comparison. In the Gemini API the price up to 200K tokens is $2/$12; above that threshold it is $4/$18. Cache is billed separately, including storage.
The model is convenient when Google AI Studio/Cloud, multimodal pipelines, and long context matter. But Preview status means there is a risk of changes in behavior, limits, and endpoint name; for production, you need a pinned configuration and regression testing.
How much the same task costs
A per-million price is hard to compare at a glance. Let's take one workload: 100,000 uncached input + 20,000 output tokens. Output includes reasoning tokens. We do not include taxes, web search, tools, batch, cache storage, or discounts.
Formula:
cost = 0.1 × input price + 0.02 × output price.
| Model | Calculation | Task cost |
|---|---|---|
| MiniMax M3 | 0.1×0.30 + 0.02×1.20 | $0.054 |
| DeepSeek V4 Pro | 0.1×0.435 + 0.02×0.87 | $0.061 |
| GLM-5.2 | 0.1×1.40 + 0.02×4.40 | $0.23 |
| Grok 4.5 | 0.1×2 + 0.02×6 | $0.32 |
| Qwen3.7-Max | 0.1×2.50 + 0.02×7.50 | $0.40 |
| Gemini 3.1 Pro | 0.1×2 + 0.02×12 | $0.44 |
| Kimi K3 | 0.1×3 + 0.02×15 | $0.60 |
| Claude Opus 5 | 0.1×5 + 0.02×25 | $1.00 |
| GPT-5.6 Sol | 0.1×5 + 0.02×30 | $1.10 |
| Claude Fable 5 | 0.1×10 + 0.02×50 | $2.00 |
This is Calculated, not a bill from the provider. The real metric is cost per accepted task:
(API + tools + retries + human review) / number of accepted outputs.
For example, MiniMax is about 18.5 times cheaper than Opus on the selected token basket. But if the task requires four retries, manual edits, and a separate fact check, the advantage shrinks. That is why you should run a pilot first, then choose a plan.
What is the system-level difference between Chinese and American models
Quality: the U.S. leads at the top, China is catching up fast
The top three spots in the overall index belong to American models. But Kimi K3 is already between GPT-5.6 Sol and Grok 4.5, and GLM-5.2 is close to the American frontier group. It is more accurate to think about the gap not in terms of “countries,” but in terms of specific tasks and reasoning modes.
Price: Chinese inference is more aggressive
DeepSeek and MiniMax are an order of magnitude cheaper than Opus/Fable in a typical basket. One exception is Kimi K3: its output already costs $15 per million, so China is not always automatically cheaper.
Open weights: China’s advantage
GLM-5.2 and DeepSeek V4 Pro are available under the MIT license, so they can be deployed outside the first API. Among the selected American five, all models are closed. For a company in Russia, this is critical: self-hosting removes the risk of a sudden SaaS account block, although it adds GPU, MLOps, security, and update overhead.
Product ecosystem: advantage of American platforms
OpenAI, Anthropic, and Google usually provide mature tools, observability, enterprise contracts, and integrations. Chinese providers are closing the gap quickly, and Model Studio offers OpenAI-compatible endpoints, but documentation, regions, and data processing terms must be checked for each specific deployment.
Censorship and answer policy differ across all providers
It would not be honest to reduce the issue to the slogan “China censors, the U.S. does not.” Every provider has restrictions, but the topics, rules, and mechanisms differ. For politically, legally, or medically sensitive tasks, you need your own test for refusals, citations, and completeness — in Russian and using your own phrasing.
How to use the models from Russia: four routes
As of August 9, 2026, Russia is not listed among the officially supported countries for OpenAI, Anthropic and Gemini API. OpenAI warns directly that access from an unsupported country may lead to account blocking or suspension.
Route 1. A Russian aggregator with ruble payments
The simplest technical path is a provider that accepts rubles and provides upstream access itself. For example, ProxyAPI says it works without a VPN. In the price list as of 09/08/2026, including VAT, the following rates per million tokens are listed:
| Model | Input/output, ₽ per 1M | Our 100K+20K basket |
|---|---|---|
| Claude Opus 5 | 1,516 / 7,579 | ≈303 ₽ |
| Claude Fable 5 | 2,100 / 10,500 | ≈420 ₽ |
| GPT-5.6 Sol | 800 / 4,700 | ≈174 ₽ |
| Gemini 3.1 Pro up to 200K | 600 / 3,640 | ≈133 ₽ |
Pros: Russian payment, one API, no need to maintain foreign accounts. Cons: markup, an additional data processor, and dependence on the intermediary's catalog. Before sending client documents, review the contract, storage, subprocessors, logging, and data deletion.
Route 2. An international router
OpenRouter combines many models, accepts cards, Alipay, and USDC; as of the check date, the service lists a 5.5% top-up fee. This is convenient for a single API and failover between providers, but a Russian bank card does not usually become international just because of a router. In addition, the request passes through an extra layer and the selected inference provider.
Route 3. A direct account, VPN, and foreign card
A technically stable VPN and a foreign card can open the page and complete the payment. This does not make Russia a supported country and does not override the provider’s Terms. This route makes sense only if the user has a lawful basis for an account in a supported country: real residence, a company, a bank account, or another truthful set of details allowed by the service rules.
If you knowingly accept the risk of being blocked:
- Use a paid VPN with a kill switch and a consistently supported country; free VPNs are risky because of leaks, overloaded IPs, and frequent geolocation changes.
- Do not bounce an account between a dozen countries. The IP, account country, card BIN, phone number, and billing address should form a truthful and consistent profile.
- Enable MFA, save your recovery codes, and export important chats/settings. Treat a sudden suspension as a real possibility.
- Do not send confidential data through an unknown VPN. Even over HTTPS, the operator can see connection metadata, and a malicious app can install its own certificate.
- For business, do not build a critical workflow on a single consumer account: you need a second provider route and portable prompts/evals.
We do not recommend falsifying your country of residence, documents, or address: it creates a risk of blocking, balance loss, and legal issues.
Route 4. Chinese open-weight model in your own environment
GLM-5.2 or DeepSeek V4 Pro can be deployed with an available inference provider or on-premise. This is the most independent route for sensitive data and production, but not necessarily the cheapest at low volume. Compare GPU/hour, utilization, redundancy, engineering time, and update costs against the API.
More on the architecture and intermediary risks — in our comparison of low-cost LLM APIs.
Virtual card for neural networks: what to actually check
A virtual card is just a standard payment card without plastic, not a universal “workaround.” A VPN changes your IP, but it does not change the card BIN, the owner’s KYC, the issuer country, or the billing address.
Before issuing a card, check:
- the issuer opens the card in your name and legally serves you;
- it is Visa/Mastercard or another network accepted by the specific provider;
- online, international, recurring payments, and 3-D Secure are allowed;
- there is a real billing address assigned by the bank/issuer; you cannot make one up;
- issuance, top-up, FX conversion, refund, and inactivity fees are clear;
- the card is not one-time use and will not change details before the next charge;
- the service returns the remaining balance and provides support in case of a declined payment.
Practical process: first complete KYC, then fund with a small amount, test a $5–10 payment or the minimum API credit, and only then subscribe. Do not keep a large balance with a card intermediary and do not buy other people’s accounts. A foreign bank card legally opened in the owner’s name is usually more reliable than anonymous “virtual plastic” from Telegram.
Red flags: “no KYC forever,” a shared billing address for thousands of customers, top-ups only in irreversible crypto, no legal entity or pricing, and selling a ready-made account together with cookies.
How to choose the right model for the task
| Task | Primary candidate | Budget candidate | What to measure |
|---|---|---|---|
| Complex analysis and strategy | Claude Opus 5 | Kimi K3 / GLM-5.2 | accuracy, citations, review time |
| Development and agentic coding | GPT-5.6 Sol / Opus 5 | GLM-5.2 / DeepSeek V4 Pro | passed tests, regressions, PR cost |
| High-volume extraction and classification | Grok 4.5 | MiniMax M3 / DeepSeek V4 Pro | F1, JSON validity, ₽ per 1,000 documents |
| Long documents and RAG | Kimi K3 / Gemini 3.1 Pro | GLM-5.2 | recall, faithfulness, context cost |
| Sensitive enterprise data | enterprise API with a contract | GLM/DeepSeek on-premise | data path, deletion, SLA, recovery |
| Access from Russia without a foreign card | ruble-denominated aggregator | self-host/open-weight | markup, data terms, backup route |
A minimal pilot is not “five nice prompts,” but 50–200 real anonymized cases:
- Define the correct answer or acceptance criteria before launch.
- Run the same prompts and tool definitions across 3–5 models.
- Calculate first-pass acceptance, critical errors, latency p50/p95, and total tokens.
- Add the cost of manual review and retries.
- Test provider-route failure and switching to the backup route.
- Choose a primary low-cost model and an escalation model for complex requests.
If token volume grows, use caching, batch, and routing. The economics are covered in detail in the article how to keep AI costs stable.
Frequently asked questions
Which Chinese AI model is the best in August 2026?
According to the Artificial Analysis Intelligence Index v4.1, Kimi K3 leads with 57 points. For self-hosting and open licensing, GLM-5.2 is more practical, and for the lowest API price, DeepSeek V4 Pro or MiniMax M3. The answer depends on the task.
Which American model is the best?
Claude Opus 5 leads with 61 points. GPT-5.6 Sol is convenient if you already use the OpenAI stack, Grok 4.5 offers a lower price, and Gemini 3.1 Pro fits the Google ecosystem. Fable 5 should be chosen only after your own cost/quality test.
Can you use ChatGPT from Russia through a VPN?
Technically, the site may open, but Russia is not on OpenAI’s official list of supported countries. The company warns about the risk of blocking when accessing from an unsupported country. A VPN does not guarantee access and does not solve the card, KYC, or Terms issue.
How do you pay for a foreign AI service with a Russian card?
Directly, Russian cards are usually not accepted. Practical options are an aggregator with ruble payment, a legally issued foreign card, or an international router with an available top-up method. Sending money to a “middleman in chat” and buying someone else’s account carry high risk.
Are Chinese models safer for Russia?
Not automatically. Regional access may be easier, and open weights reduce vendor lock-in. But a cloud Chinese API is still an external data processor. Security is determined by the contract, logging, retention, encryption, and architecture—not by the country flag.
Can the best model be installed locally?
The top five American models in the ranking cannot—their weights are closed. GLM-5.2 and DeepSeek V4 Pro can be deployed independently under their license terms. You will need suitable GPUs or an inference cloud, MLOps, monitoring, and your own evals.
How AI Dawn helps you choose and implement an LLM
AI Dawn starts not with buying a subscription, but with a measurable task. As part of a pilot, the team can take four concrete actions:
- Build an anonymized test set and acceptance criteria.
- Compare 3–5 models by acceptance rate and total cost per successful task.
- Design a router with a backup provider and a separate RAG/on-premise environment for sensitive data.
- Integrate the selected route with your CRM, 1C, or internal system and add monitoring.
A safe first step: choose one process, record the current cost/speed, data sources, constraints, and the criterion for "the result is accepted without revisions." That is enough for a pilot to answer the question in dollars and cents, not impressions from chat.
Bottom line
In August 2026, American models remain at the top of the overall rankings: Opus 5, Fable 5, and GPT-5.6 Sol take the top three spots. China’s Kimi K3 is already close behind, GLM-5.2 offers the strongest open-weight balance, and DeepSeek V4 Pro and MiniMax M3 dramatically cut the cost of high-volume tasks.
For users in Russia, the choice cannot end with benchmarks. Check four layers: quality on your own data, cost per accepted task, legal and reliable access, and the processing environment. VPNs and virtual cards may help technically, but they do not change the list of supported countries and should not rely on false information. For a personal experiment, an aggregator is convenient; for business, use a contracted API with failover or an open-weight model in a controlled environment.
Start with 50–200 test tasks and two routes: a low-cost primary and a strong backup. That is how the ranking turns from a list of brands into a working architecture.