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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallShort answer: Alibaba’s January 2025 claim referred to Qwen2.5-Max, a large hosted mixture-of-experts model—not the standard open-weight Qwen2.5-72B. Alibaba reported that Qwen2.5-Max outperformed DeepSeek-V3 on Arena-Hard, LiveBench, LiveCodeBench and GPQA-Diamond. That was a vendor-reported, benchmark-specific result—not proof that every Qwen2.5 model beat every DeepSeek model, and not evidence that Qwen2.5-Max defeated DeepSeek-R1.
For developers, businesses and investors evaluating AI costs, the practical lesson is more useful than the headline: identify the exact model, verify current pricing and availability, and compare the model against the workload you actually need.
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The claim in one table
| Question | Accurate answer |
|---|---|
| Which Alibaba model? | Qwen2.5-Max |
| Which DeepSeek model? | DeepSeek-V3 |
| Who reported the comparison? | Alibaba, in its January 28, 2025 announcement |
| Where did Alibaba report wins? | Arena-Hard, LiveBench, LiveCodeBench and GPQA-Diamond |
| Was it a universal victory? | No. The comparison covered selected benchmarks and did not establish superiority for every task, price or deployment model. |
| Did Qwen2.5-Max beat DeepSeek-R1? | Not according to the cited announcement. R1 was a separate reasoning-focused model. |
Alibaba’s original announcement is the primary source for the Qwen2.5-Max comparison. Its wording should be treated as a company-reported evaluation rather than an independent industry ranking.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsQwen2.5 and Qwen2.5-Max are not the same model
The biggest source of confusion is the name. Qwen2.5 is a family of open-weight, dense language models ranging from 0.5 billion to 72 billion parameters. The family also included specialized products such as Qwen2.5-Coder, Qwen2.5-Math, Qwen2.5-VL, Qwen2.5-Omni and Qwen2.5-1M. Alibaba’s Qwen2.5 announcement describes the family and its model-specific licensing terms.
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Qwen2.5-Max was a separate, much larger hosted mixture-of-experts model. Alibaba said it had been trained on more than 20 trillion tokens, followed by supervised fine-tuning and reinforcement learning from human feedback. At launch, Alibaba made it available through Qwen Chat and Alibaba Cloud’s API.
| Model | Model type | Typical access | Why it matters |
|---|---|---|---|
| Qwen2.5-72B-Instruct | Open-weight dense model | Download, self-hosting or third-party hosting | The flagship standard Qwen2.5 checkpoint |
| Qwen2.5-Max | Large hosted MoE model | Alibaba services and API access at launch | The model behind the January 2025 benchmark claim |
| DeepSeek-V3 | Open-weight MoE model with hosted API access | Download, DeepSeek API or third-party hosting | The principal target of Alibaba’s comparison |
| DeepSeek-R1 | Reasoning-focused model | API, weights and distilled variants | A different model class and evaluation question |
Therefore, the statement “Qwen2.5 beat DeepSeek” is incomplete. It can incorrectly suggest that the downloadable Qwen2.5-72B model won the comparison, when the cited claim concerned Qwen2.5-Max.
Which DeepSeek model was involved?
The comparison was with DeepSeek-V3. It was not a documented head-to-head victory over DeepSeek-R1.
That distinction matters because V3 and R1 were designed and evaluated differently. DeepSeek-V3 is a general-purpose model, while DeepSeek-R1 emphasizes reasoning and test-time computation. DeepSeek’s R1 release announcement separately describes the reasoning model and its distilled versions, including models derived from Qwen2.5-based systems.
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R1’s existence also makes broad “best model” claims difficult. A general instruction model may be preferable for fast, routine generation, while a reasoning model may be preferable for difficult mathematical, scientific or multi-step problems. They should not be treated as interchangeable simply because both are large language models.
What Alibaba reported on the benchmarks
The following summary reflects Alibaba’s published comparison. It does not reproduce numerical scores because the available source evidence establishes the direction of the claims more clearly than every underlying number.
| Benchmark | What it broadly measures | Alibaba’s reported result | Important qualification |
|---|---|---|---|
| Arena-Hard | Difficult prompts evaluated through preference-style comparisons | Qwen2.5-Max ahead of DeepSeek-V3 | Results can vary with prompts, evaluators and judging setup |
| LiveBench | Broad capability testing using fresher questions | Qwen2.5-Max ahead | Benchmark versions and test configurations can change |
| LiveCodeBench | Coding performance with contamination controls | Qwen2.5-Max ahead | Pass criteria, sampling and test settings affect results |
| GPQA-Diamond | Difficult graduate-level science questions | Qwen2.5-Max ahead | A narrow score difference is not proof of broad superiority |
| MMLU-Pro | Advanced knowledge and reasoning | Competitive with DeepSeek-V3 | “Competitive” is weaker than “surpassed” |
Alibaba also compared Qwen2.5-Max with systems including GPT-4o, Claude 3.5 Sonnet, Llama 3.1-405B and Qwen2.5-72B. Those comparisons provide context, but they do not turn the announcement into a neutral leaderboard.
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How strong was the evidence?
The available primary evidence is Alibaba’s own evaluation. That does not make the results meaningless, but it changes how they should be described. The defensible wording is “Alibaba reported that Qwen2.5-Max led DeepSeek-V3 on several benchmarks.” The indefensible shortcut is “Qwen definitively beat DeepSeek.”
Benchmark results can differ because of:
- Prompt wording and formatting
- System prompts and answer templates
- Sampling temperature and number of attempts
- Whether tools, retrieval or other external systems were enabled
- Model snapshots and API versions
- Base versus instruction-tuned variants
- The evaluator model used in preference tests
- Benchmark contamination and familiarity with public test sets
- Whether small score differences are statistically meaningful
A fair comparison should therefore identify the benchmark version, model variant, testing configuration and date. Even a carefully run benchmark is a measurement of selected capabilities, not a complete description of quality, reliability or business value.
How Qwen2.5-72B compared with DeepSeek-V3
Qwen2.5-72B is a separate question from Qwen2.5-Max. DeepSeek’s DeepSeek-V3 documentation includes comparisons involving Qwen2.5-72B-Instruct and presents V3 as competitive with earlier open-weight models in several evaluations.
This distinction has practical consequences:
- Qwen2.5-72B was a downloadable 72-billion-parameter dense model.
- Qwen2.5-Max was Alibaba’s larger hosted MoE challenger.
- DeepSeek-V3 was the model targeted by Alibaba’s January comparison.
- DeepSeek-R1 was a separate reasoning-focused release.
A benchmark win by a hosted frontier model does not automatically tell a business how a downloadable 72B checkpoint will perform after quantization, fine-tuning or local deployment.
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For API buyers
The announcement showed that Alibaba Cloud offered another OpenAI-compatible route to a high-capability model. Alibaba’s launch example used model identifier qwen-max-2025-01-25 and the international DashScope-compatible endpoint https://dashscope-intl.aliyuncs.com/compatible-mode/v1. Model identifiers, regions and endpoints can change, so this should not be treated as a guaranteed current interface in 2026.
Before committing money or production traffic, compare:
- Current model name and snapshot
- Input and output token prices
- Context window and rate limits
- Latency and reliability
- Region availability and data-retention terms
- Tool calling and structured-output support
- Whether the production endpoint is the same model evaluated in the announcement
Alibaba Cloud’s Model Studio is the relevant managed-service route for Qwen models. Its catalog and prices should be checked directly because newer models can replace older entries.
For local deployment
Qwen2.5-72B, smaller Qwen2.5 variants, DeepSeek-V3 and DeepSeek-R1 distilled models are more relevant than Qwen2.5-Max when the requirement is downloadable weights or offline inference. Local deployment can improve control over privacy and data residency, but large models may require substantial GPU memory, quantization and engineering work.
Open weights are not the same as unrestricted open source. Check the terms for the specific checkpoint. Alibaba’s Qwen2.5 release included model-specific licensing caveats, including different treatment for some sizes. DeepSeek’s R1 repository documents its released and distilled models.
For cost-conscious teams
Do not infer that Qwen2.5-Max was cheaper or more expensive than DeepSeek from the benchmark claim alone. No equivalent Qwen price is established here. API economics depend on token prices, caching, output length, context size, rate limits, infrastructure and the amount of work needed to obtain a correct answer.
As a pricing signal, DeepSeek’s official pricing page listed deepseek-reasoner at $0.14 per million cache-hit input tokens, $0.55 per million cache-miss input tokens and $2.19 per million output tokens when observed during the research period. Prices and model names can change; buyers should verify the current figures and applicable region at the official pricing page.
For Chinese-language applications
Qwen has strong Chinese-language positioning, but English-language benchmark results should not be used as a substitute for testing a Chinese-language workload. A serious evaluation should use representative prompts, domain terminology, safety requirements and a relevant Chinese-language benchmark.
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What changed after the announcement?
The January 2025 result is now a historical snapshot rather than a current overall ranking. Alibaba later released the Qwen3 generation, including open-weight MoE models such as Qwen3-235B-A22B and Qwen3-30B-A3B. Alibaba’s later materials also position newer Qwen systems against contemporary models, reflecting how quickly the leaderboard changes. See the Qwen3 announcement for the later-generation context.
That does not erase the significance of Qwen2.5-Max. It showed that Alibaba could field a credible competitor to DeepSeek-V3 on several public evaluations and gave developers another API option. But a 2025 launch comparison should not be used in 2026 to choose a model without checking current releases, prices and service terms.
A practical comparison checklist
- Name the exact model. Do not treat Qwen2.5-72B, Qwen2.5-Max and Qwen3 as interchangeable.
- Name the exact competitor. Confirm whether the comparison is with DeepSeek-V3, DeepSeek-R1 or another release.
- Match the benchmark to the job. Use coding tests for coding, reasoning tests for reasoning and production samples for your actual workflow.
- Check access. Determine whether the model is downloadable, hosted-only or available through a third-party provider.
- Review license and privacy terms. These can matter more than a small benchmark advantage.
- Calculate total cost. Include input tokens, output tokens, caching, infrastructure, monitoring and engineering time.
- Test the current endpoint. An API may serve a newer snapshot than the one used in a launch announcement.
Bottom line on the headline
Alibaba’s Qwen2.5-Max appears to have been a credible challenger to DeepSeek-V3 in Alibaba’s selected benchmark comparison. But “Alibaba’s Qwen 2.5 surpassed DeepSeek” was too broad: the result involved one specific Qwen model, one specific DeepSeek model, selected tests and a vendor-reported methodology. It did not establish that Qwen2.5-72B beat DeepSeek-V3, that Qwen2.5-Max beat DeepSeek-R1, or that Alibaba’s model was the best choice for every budget, language, workload or deployment environment.
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