DeepSeek vs Qwen vs Yi vs Baichuan: 2026 Selection Guide

There is no evidence-based “best pick” among DeepSeek, Qwen, Yi and Baichuan without naming exact checkpoints and testing them on the same hardware. In 2026, these families differ too much in age, scale, purpose and licence terms for a brand-level ranking to be meaningful.

The defensible approach is narrower. Choose checkpoints that fit your hardware and commercial constraints, freeze one serving configuration, and compare their failures on your own Chinese-language tasks. For a new general-purpose deployment, Qwen and DeepSeek currently offer the most recent general checkpoints in this four-family set. Yi remains a smaller, older option with straightforward Apache 2.0 terms for the checkpoint examined here. Baichuan’s current releases need extra care: its newer Baichuan-M3 model is medical-focused, while the general-purpose Baichuan2 checkpoint is older and subject to a model community licence.

This guide is a selection framework, not an original benchmark. It does not publish invented throughput, latency or quality scores. Specifications and licence links were checked on 13 August 2026; vendor-controlled details can change, so verify the model card and licence again before deployment.

Table of Contents

1. Why a brand-level winner would be misleading

2. The exact checkpoints in this 2026 comparison

3. What the verified specifications mean in practice

4. Choose by constraint before testing quality

5. A reproducible benchmark protocol

6. Two realistic selection scenarios

7. Licensing and China deployment boundaries

8. Common comparison mistakes

9. When these four families are the wrong shortlist

10. Frequently Asked Questions

11. A defensible next step

Why a brand-level winner would be misleading

“DeepSeek vs Qwen” sounds like a product comparison. It is closer to comparing two catalogues. Each family contains base models, instruction-tuned models, reasoning variants, specialist checkpoints, hosted APIs and third-party quantisations. A result from one checkpoint says little about another checkpoint carrying the same brand.

The four names are not a neutral “Big Four” for 2026, either. They are a historically familiar shortlist, not a complete map of current Chinese open-weight models. Within this shortlist, release cadence is uneven. DeepSeek and Qwen have recent general-purpose releases; the official Yi repositories examined for this article still centre on Yi-1.5 from 2024; and Baichuan’s newer M3 release is aimed at medical use rather than general business chat.

Scale creates a second problem. A 9-billion-parameter dense model and a 284-billion-parameter mixture-of-experts model do not belong on the same latency chart unless the hardware, quantisation and serving settings are disclosed. “Active parameters” can help explain compute per generated token, but the deployment still has to store or stream the full set of model weights. A model with 13 billion active parameters is not equivalent to a 13-billion-parameter dense model.

Finally, vendor benchmark tables are useful for understanding what a model developer chose to measure, but they are not a substitute for a product-shaped evaluation. Different prompts, judging models, agent scaffolds, context lengths and generation budgets can change results. This article therefore uses official sources for specifications and licences, while leaving quality conclusions to a reproducible local test.

The exact checkpoints in this 2026 comparison

The table deliberately compares exact repositories. It is a catalogue for building a shortlist, not a performance ranking.

Two scope notes matter. First, Qwen’s official model catalogue was moving beyond Qwen3.6 at the verification date. A newly listed model is not automatically the right production baseline; maturity of documentation, runtime support and your own retest matter more than a version number. Second, Baichuan-M3-235B is a 2026 release, but its official description identifies it as a medical-enhanced model. It should be evaluated for a medical workflow, not used to refresh a general-purpose comparison by name alone.

The Yi licence point also needs precision. Older descriptions of the Yi family can refer to community terms, but the current official Yi-1.5 repository states that the Yi-1.5 code and weights are distributed under Apache 2.0. Licence analysis must follow the exact checkpoint, not a family-wide memory of earlier releases.

What the verified specifications mean in practice

DeepSeek-V4-Flash-0731: current, capable and infrastructure-heavy

DeepSeek-V4-Flash-0731 is the most recent DeepSeek checkpoint used here. The V4 technical report describes a 284-billion-parameter backbone with 13 billion parameters active per token and a one-million-token context window. The 0731 repository includes an attached DSpark speculative-decoding module, which is why its repository metadata reports 304 billion parameters. The checkpoint is published in a mixed low-precision format and licensed under MIT.

Those facts do not establish that DeepSeek is “best for agents.” They establish that the checkpoint was designed and documented for reasoning and agent-oriented workloads, includes a tool-call format, and has a much larger storage and serving footprint than the Yi or Baichuan checkpoints in this comparison. The only reliable way to decide whether the architecture helps your agent is to measure successful tool completion, error recovery and end-to-end latency in your own harness.

The million-token limit also should not become a default serving setting. KV-cache requirements grow with context, and long-context claims do not prove that retrieval quality remains acceptable at the maximum. Start with the smallest window that covers the real task, then test longer inputs separately.

Qwen3.6-35B-A3B: a smaller current baseline with multimodal baggage

Qwen3.6-35B-A3B has 35 billion total parameters and 3 billion active parameters. Its official model card gives a 262,144-token native context and documents longer extrapolated operation. The checkpoint is Apache 2.0 and includes serving examples for SGLang and vLLM.

Qwen is often described as an “enterprise” choice, but that label is not a measured capability. The verifiable advantage is different: this checkpoint has explicit deployment recipes, a text-only serving option and a substantially smaller total parameter count than DeepSeek-V4-Flash-0731. That can make it a practical baseline when the team wants a recent model but cannot justify DeepSeek’s weight footprint.

Qwen3.6 is also a vision-language model. If the product is text-only, benchmark it with the vision path disabled, as the official card allows. Otherwise, memory used by multimodal components can distort a text comparison.

Yi-1.5-9B-Chat-16K: a resource-bounded candidate, not a current frontier proxy

Yi-1.5-9B-Chat-16K is a 9-billion-parameter dense model with a 16K context window. It is materially easier to place on modest hardware than the larger MoE checkpoints, and its official code and weights use Apache 2.0.

That is enough to justify testing Yi when hardware or operational simplicity is the first constraint. It is not enough to claim superior cost efficiency. Cost per accepted answer depends on quantisation quality, prompt length, batching, concurrency, output length and the number of retries. A smaller model that fails structured output twice can cost more than a larger model that succeeds once.

Release age must appear in the decision. Yi-1.5 is a 2024 checkpoint. A team can rationally keep it when it already meets a stable task, but a new deployment should not treat it as equivalent in recency to DeepSeek V4 or Qwen3.6.

Baichuan2-13B-Chat: a legacy general model with a licence gate

Baichuan2-13B-Chat remains available as a 13-billion-parameter Chinese-English chat checkpoint. Its official configuration sets a 4,096-token maximum, and the project supplies a 4-bit chat version.

The commercial issue is more important than the model’s brand reputation. The official Baichuan2 repository says community use must follow both Apache 2.0 and the Baichuan2 Model Community Licence. It describes an application process and conditions involving daily active users, software or cloud service providers, transfer and sublicensing. A commercial team should obtain a written legal reading of the exact intended use before investing in integration.

Baichuan2 can still be a valid compatibility target for an existing deployment. For a new general-purpose product, however, its 2023 release date, short context and commercial approval process are reasons to demand a specific advantage in your test results before selecting it.

Choose by constraint before testing quality

Model evaluation becomes faster when hard constraints eliminate impossible candidates first.

Start with the licence. If your product redistributes weights, provides hosted model access, operates as software-as-a-service or may cross a stated user threshold, record that business model in the licence review. “Commercial use allowed” is not a complete answer when approval, field-of-use or sublicensing terms apply.

Next, set the infrastructure envelope. Record the accelerators you can actually procure, memory per device, tensor or pipeline parallel limits, acceptable cold-start time, power budget and the maximum context you need in normal traffic. Do not choose hardware after selecting a model from a leaderboard.

Then define the product contract. A support assistant may need natural Chinese, stable citations and low refusal rates on benign complaints. An extraction service may care mainly about schema validity. An agent needs correct tool selection, arguments and recovery after tool failure. These are different evaluations.

Only after those gates should quality testing begin. A reasonable initial shortlist from these four families might be Qwen3.6-35B-A3B as a recent, smaller-total-parameter baseline; DeepSeek-V4-Flash-0731 when the infrastructure can support it; and Yi-1.5-9B-Chat-16K when the test must include a smaller dense option. Add Baichuan2 only when an existing dependency, licence approval or domain result gives a concrete reason.

That is a shortlist, not a winner declaration.

A reproducible benchmark protocol

A useful benchmark makes another engineer able to reproduce both the result and the failure. Record the exact repository ID and revision hash; model and code licences; weight format and quantisation method; accelerator model and count; driver and CUDA versions; inference runtime and version; tensor and pipeline parallel settings; maximum context; batch and concurrency; system prompt; chat template; temperature, top-p, top-k, seed and output limit; prompt-set version; raw request and response logs; warm-up policy; VRAM peak; time to first token; end-to-end P50 and P95 latency; decode tokens per second; error and retry counts; test date; scorer version; and known limitations.

Do not compare a vendor API with a self-hosted checkpoint under the same model name unless the provider confirms the served revision and the article labels the two paths separately. Hosted moderation, hidden prompts and routing can change behaviour.

Build product-shaped test sets

Use frozen prompts and expected outcomes. The following examples show the shape of a useful suite; a publishable benchmark should include enough varied cases to prevent one prompt from deciding the result.

ZH-WRITE: Rewrite a late-delivery notice in 120–160 Chinese characters.
Preserve the order number, date and refund policy. Do not add compensation.

CODE-SWITCH: Summarise a Chinese support ticket while preserving the exact
English product names, API fields and error codes.

JSON: Return only JSON matching a supplied schema. Include strings containing
quotes, null values, arrays and an unknown field that must be rejected.

TOOLS: Choose one tool from a fixed catalogue, produce valid arguments, then
recover after the tool returns a timeout or a structured validation error.

ZH-RAG: Answer only from numbered Chinese passages. Cite passage IDs for every
claim and return "资料不足" when the evidence does not support an answer.

CODE: Repair a repository-level defect with visible and hidden tests. Record
tests passed, invalid edits and tool steps rather than using a judge impression.

SAFETY: Separate benign sensitive requests, ambiguous requests and clearly
disallowed requests. Measure both over-refusal and unsafe compliance.

Chinese writing should be assessed blind by at least two fluent reviewers using a defined rubric for factual preservation, fluency, tone and instruction adherence. Do not ask reviewers which brand they prefer. Resolve scoring disagreements before revealing model identities.

For Chinese-English code-switching, use exact-match checks for product names, identifiers and quoted logs, then a human score for whether the surrounding Chinese remains clear. A model that writes elegant prose but changes order_status to orderState has failed the task.

Strict JSON should be machine-scored. Report the percentage of outputs that parse, validate against the schema and contain no extra text. If a repair step is allowed, report first-pass and repaired success separately.

Tool use needs two scores: initial call correctness and recovery correctness. A model should not receive full credit for selecting the right function if it invents an argument after a timeout. Record the complete tool transcript.

RAG faithfulness should score claim support, citation correctness and abstention when evidence is missing. Retrieval must be frozen across models; otherwise the test mixes retriever quality with generator quality.

Separate latency from generation speed

Tokens per second is not user-perceived latency. Publish time to first token, end-to-end P50 and P95, output length and concurrency. Warm and cold runs should be separate. For MoE models, disclose expert parallelism and any CPU or NVMe offload.

VRAM also needs a measured peak, not an estimate from parameter count. Report model memory, KV cache and runtime overhead where the serving engine exposes them. A one-million-token configuration that starts successfully but supports only one concurrent request may be irrelevant to the product.

Publish the evidence or remove the ranking

If this page later adds “best” recommendations, it should also publish the prompt set, raw outputs, scoring rubric, evaluation script, runtime configuration and test date. A public repository is the cleanest format. Until those artefacts exist, recommendations should stay conditional: “fits this licence and hardware envelope” rather than “wins Chinese writing.”

Two realistic selection scenarios

Scenario 1: an internal Chinese document assistant

Assume a team needs a text-only assistant over internal Chinese documents, has a 16K normal context target and prioritises schema-valid citations. The team has one 24 GB accelerator for the pilot.

DeepSeek-V4-Flash-0731 and the official BF16 Qwen3.6-35B-A3B checkpoint exceed that simple single-device envelope without aggressive quantisation or offload. Yi-1.5-9B-Chat-16K is the straightforward checkpoint to test first because it fits the context target and has Apache 2.0 terms. Baichuan2-13B-Chat is less attractive because its official context is 4K and its commercial terms require separate review.

This does not prove Yi will produce the best answers. It shows that hardware, context and licence constraints can decide which model reaches the quality test.

Scenario 2: a multi-GPU tool-using service

Assume a team can operate a multi-GPU server and needs long sessions, tool calls and repository-level coding. It should compare Qwen3.6-35B-A3B and DeepSeek-V4-Flash-0731 under the same tool catalogue and error-injection suite. Qwen can be served in text-only mode; DeepSeek requires a larger weight footprint but publishes an agent-oriented tool format.

The decision should follow measured successful task completions per unit of latency and cost, not vendor benchmark claims. If Qwen completes 98 of 100 tasks with fewer retries on the available hardware, its smaller deployment may win that product. If DeepSeek recovers from complex tool failures that Qwen misses and the additional infrastructure is acceptable, DeepSeek may be the better fit. Those numbers are illustrative decision logic, not results from this article.

Licensing and China deployment boundaries

“Open source,” “open weights” and “available for download” are not interchangeable legal conclusions. Review the licence attached to the exact revision you deploy, including model weights, repository code, tokenizer, bundled assets and third-party quantisation. Keep a copy of the reviewed terms with the release record.

Apache 2.0 and MIT are permissive licences, but they do not remove obligations arising from data protection, content rules, sector regulation or contracts. Baichuan2 adds a more obvious checkpoint-specific issue because its community licence describes approval and commercial conditions. This is a commercial legal question, not something a benchmark can resolve.

Model choice also does not make a public generative-AI service compliant in mainland China. China’s Interim Measures for Generative Artificial Intelligence Services apply according to the service and deployment context, not merely the origin of the weights. Public-service filings, data handling and content controls need their own assessment. Obtain advice from counsel qualified in Chinese technology and privacy law before launch.

Safety and refusal behaviour should be measured at the deployed-system level. The weights, system prompt, retrieval layer, moderation controls and hosting provider can all change the outcome. A refusal rate reported for a hosted chatbot should not be presented as a property of a self-hosted checkpoint.

Common comparison mistakes

The first mistake is comparing families instead of revisions. “Qwen” could mean a small dense text model, a large MoE model, a multimodal checkpoint or an API alias. Put the repository ID and revision hash in every chart.

The second is using active parameters as a memory figure. Active parameters describe part of per-token computation in an MoE model; all expert weights still have to be stored or made available. Report measured memory and the full parallelism configuration.

The third is benchmarking different quantisations and calling the result a model comparison. Quantisation can change accuracy, speed and memory. If 4-bit deployment is the product constraint, quantise every candidate with a documented method and treat each quantised artefact as its own checkpoint.

The fourth is scoring JSON by visual inspection. An output that looks like JSON can still fail parsing, schema validation or downstream business rules. Use a parser and publish first-pass success.

The fifth is allowing different output budgets. Reasoning models can consume far more tokens. Fix the maximum, report actual output length and include total task latency.

The sixth is treating a long context window as proof of good RAG. Retrieval, chunking and citation discipline remain separate. Test support for each generated claim and keep retrieval results identical across models.

The seventh is hiding old checkpoints behind a current year. Yi-1.5 and Baichuan2 may still be useful, but their release dates belong in the comparison. “2026 selection guide” describes the decision date, not the vintage of every model.

When these four families are the wrong shortlist

Do not use this four-family comparison as a market-wide buyer’s guide. It excludes other Chinese and international open-weight families, hosted-only services and specialist models. Expand the shortlist when another checkpoint better matches the language, modality, hardware or sector.

Avoid self-hosting when the team cannot own patching, capacity planning, monitoring, abuse controls and incident response. A managed API may cost more per token while reducing operational risk. Compare total operating cost, not only weight-download access.

Do not select Baichuan-M3 for a general assistant solely because it is newer than Baichuan2. Its official positioning is medical. Conversely, a healthcare team should not rely on a general benchmark to approve a medical model; clinical safety, validation and local regulation require a separate programme.

Delay model selection when the training or evaluation data cannot be handled lawfully, when the intended commercial use has not cleared licence review, or when the team lacks a product-shaped test set. In those cases, another leaderboard run produces false precision rather than progress.

Frequently Asked Questions

Which is the best model among DeepSeek, Qwen, Yi and Baichuan in 2026?

There is no supportable universal winner without a shared benchmark and deployment configuration. For a new general product, DeepSeek-V4-Flash-0731 and Qwen3.6-35B-A3B are the current general checkpoints in this comparison; Yi-1.5 is a smaller legacy candidate, and Baichuan2 requires a stronger task-specific and licensing reason. Choose after testing the exact revisions on your hardware.

Is Qwen automatically the safest commercial choice because it uses Apache 2.0?

No. Apache 2.0 is comparatively permissive, but licence simplicity is only one gate. A commercial deployment still needs review of data rights, privacy, public-service rules, sector obligations and any third-party components. Confirm the licence stored in the exact checkpoint revision rather than relying on a family-level statement.

Does DeepSeek’s 13 billion active parameters mean it runs like a 13B model?

No. DeepSeek-V4-Flash has 284 billion total parameters and 13 billion active parameters per token. The active count helps describe MoE computation, but the full model weights still need storage or an offload strategy. Compare measured throughput, VRAM and latency on the intended serving stack.

Can Yi-1.5 still be a rational production choice in 2026?

Yes, when a stable task fits its 16K context, the 9B checkpoint meets quality requirements and smaller infrastructure materially reduces operational complexity. Its age should still be recorded. A new team should run it against at least one recent checkpoint rather than assuming lower parameter count means better cost per accepted answer.

Why is Baichuan-M3 not used as the general Baichuan comparator?

Baichuan describes M3 as a medical-enhanced model. That makes it relevant to a medical evaluation, not a like-for-like replacement for Baichuan2 in a general Chinese assistant comparison. A domain model can outperform on its intended tasks while being the wrong baseline for general chat, coding or extraction.

What is the fairest way to compare quantised checkpoints?

Use the same quantisation class and a documented conversion process where each architecture supports it, then publish the exact artefact IDs. Keep hardware, runtime, context, sampling and workload fixed. Report quality and speed changes against each model’s official precision so readers can see whether a fast result came with a material accuracy loss.

How many prompts are enough for a useful model evaluation?

There is no universal count. The set is large enough when it covers the product’s high-volume tasks, costly failures, language patterns and safety boundaries with repeated variants. A hundred representative cases with deterministic checks and reviewed failures can be more useful than thousands of unrelated benchmark questions. Preserve a hidden holdout set to reduce prompt overfitting.

Should Chinese RAG use the same generator and embedding family?

Not necessarily. Retrieval and generation should be evaluated separately. Choose the embedding and reranking stack that retrieves the correct Chinese passages, freeze those passages, and then compare generators for faithfulness, citation accuracy and abstention. Matching brand names does not guarantee better semantic alignment.

How often should the comparison be rerun?

Rerun it when a checkpoint, quantisation, serving runtime, system prompt, retrieval pipeline or hardware configuration changes. Also retest after a material shift in user traffic or failure patterns. A date-stamped result is evidence for one configuration, not a permanent ranking of a model family.

A defensible next step

Start with two checkpoints that survive licence and infrastructure review. Freeze the runtime, quantisation, prompt set and scoring rules before the first run. Include Chinese writing, code-switching, strict JSON, tool recovery, RAG faithfulness, coding and safety cases that resemble real traffic. Publish raw outputs and latency distributions if the page later names a winner.

For many teams evaluating only these four families, Qwen3.6-35B-A3B is a sensible recent baseline, DeepSeek-V4-Flash-0731 is the larger current alternative, and Yi-1.5-9B-Chat-16K is the resource-bounded control. Baichuan2 belongs in the run only when its licence and older technical envelope fit a concrete requirement.

That is the strongest recommendation the available evidence supports. The final choice should come from reproducible failures on your system, not a brand slogan or a leaderboard screenshot.