A year ago choosing a model was simple: you took the best one you could afford and started building. Today open models have come close enough that the decision is no longer automatic. The problem is that equivalences tend to be oversimplified. Saying GLM is "the open Claude" or that DeepSeek replaces GPT helps you get your bearings, but it does not tell you what to put in production. Opus and Haiku do different jobs; so do Astra, Sol and Luna. Exactly the same is true of open models. So here we compare model classes and, above all, workloads.
Data checked on 13 October 2026. To measure capability we mainly use the Artificial Analysis Intelligence Index v4.3.2 and evaluations such as GDPval-AA, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam and AA-LCR. When we say "open model" we mean open-weight models: the weights are available, although the licence, code, data and training process are not always open to the same degree.
The equivalence table
This is not a table of 1:1 replacements. It is a list of models we would put in the same comparison before making a decision.
| If you use… | We would test first… | Reading |
|---|---|---|
| Claude Opus 5.5 | GLM-5.3 / MiMo V2.6 Pro / Kimi K3 | Same class of frontier workloads. Opus keeps a higher ceiling |
| Claude Fable 5.1 | GLM-5.3 / MiMo V2.6 Pro / Kimi K3 | Worth a direct comparison in coding, agents and knowledge work |
| Claude Sonnet 5.5 | GLM-5.3 Flash / Qwen3.8-Flash-Next | For well-scoped tasks and agentic coding; GLM-5.3 if you need more reasoning |
| Claude Haiku 5.5 | Qwen3.6 / DeepSeek V4.1 Flash | Qwen for very narrow batch processing; DeepSeek when tools, vision or more difficulty come in |
| GPT-6 Astra | GLM-5.3 / MiMo V2.6 Pro / Kimi K3 | A closed frontier model against open frontier models |
| GPT-6.1 Sol | GLM-5.3 / GLM-5.3 Flash | GLM-5.3 if capability comes first; Flash if you want a better balance |
| GPT-6 Luna | DeepSeek V4.1 Flash / GLM-5.3 Flash | The most direct comparables on efficiency, agents and volume |
| Gemini 4 Argon | GLM-5.3 / MiMo V2.6 Pro / Kimi K3 | A closed frontier model against open frontier models; MiMo also covers audio and video |
| Gemini 3.8 Flash | GLM-5.3 Flash / DeepSeek V4.1 Flash | A very natural comparison for agents and production workloads |
The row that matters most to us is the first. GLM-5.3 and MiMo V2.6 Pro are the models we would put up against Opus and Astra. The overall indexes still show a gap of several points, but the distance changes a lot once you go down to more specific tests.
Against Opus 5.5 High, GLM is practically level on AutomationBench-AA (62% against 63%) and SciCode (59% against 60%), although Opus keeps a clear lead on Terminal-Bench, HLE and general capability. Against Astra Max, GLM wins on GDPval-AA (1651 against 1574) and SciCode (59% against 56%), and is practically level on long-context reasoning (80% against 81%). They are different models, but they solve the same class of problems and get close results on several tests. That is why we place them in the same segment.
Sources: Opus 5.5 vs GLM-5.3 , Opus 5.5 High vs GLM-5.3 , GPT-6 Astra vs GLM-5.3 .
There is no single best open model
Today's picture is more interesting than an overall ranking. MiMo V2.6 Pro is slightly ahead of GLM-5.3 on general capability (46 against 45) and stands out in reasoning, science and long context. GLM does better on AutomationBench and Terminal-Bench. Kimi K3 sits a little lower on aggregate capability, but it is the best of the three at long-context reasoning and document work.
The Flash range works the same way. GLM-5.3 Flash has more general capability than DeepSeek V4.1 Flash, but DeepSeek scores 69% against 60% on AutomationBench and generates more than three times as many tokens per second. As soon as the job changes, so does the model that makes the most sense. That is why the rest of this guide is organised by workload.
Choose by workload, not by vendor
Reasoning and knowledge work: MiMo V2.6 Pro
If your model spends the day analysing, researching, reasoning over complex information or preparing professional work, MiMo V2.6 Pro would be our first candidate. It is slightly above GLM on the overall index and does better on Humanity's Last Exam, SciCode and long-context reasoning. It also accepts text, image, audio and video.
It is also the most recent of these models. In a market that moves this fast, it is reasonable to expect the next releases from Z.ai or Moonshot to move the bar again.
That does not mean it replaces Opus or Astra in their most powerful configurations. When the goal is the best possible answer and cost hardly matters, closed frontier models still have the edge. MiMo becomes especially interesting when you are paying for that level of capability on tasks that do not use it: a meeting summary, the analysis of a 30-page report or a reply to a technical support ticket. It can also fit agentic coding if your daily work is building and maintaining software.
Coding and complex agents: GLM-5.3
GLM-5.3 is the open model we would use as the reference against Opus, Astra or Argon for coding. Z.ai trained it for complex coding and long-horizon tasks. In independent evaluations it is still below the best closed models when you set reasoning to the maximum, which is rare in real use because it burns through the quota very fast. In more reasonable configurations it performs very well in agentic development, science, automation and long context.
We would test it with coding agents on real repositories, multi-step technical tasks, agent orchestration and, in general, any workload where you are paying for Opus or Astra by default today.
GLM-5.3 supports 1M of context and multimodal input (text, image, video and audio), so it is not limited to coding either.
Source: Z.ai: GLM-5.3 .
Agentic coding with a better balance: GLM-5.3 Flash
If GLM-5.3 is the frontier model, GLM-5.3 Flash is a more sensible starting point for a good part of daily work. Artificial Analysis puts it at 42 points on its intelligence index, against 41 for Gemini 3.8 Flash High. On AutomationBench they tie at 60%. GLM Flash is ahead on GDPval-AA and Terminal-Bench, while Gemini keeps an edge on SciCode, HLE and PDF work.
They are two models in a similar capability zone, with different strengths. That is exactly the comparison we are interested in.
GLM-5.3 Flash has 1M of context and accepts text, images and video. At Helmcode it is our default for agentic coding sessions because it offers a lot of capability without always having to step up to the flagship.
Source: Gemini 3.8 Flash vs GLM-5.3 Flash .
Agents and tool use: DeepSeek V4.1 Flash
There are tasks where the model spends more time acting than thinking: calling APIs, running tools, updating systems, reading an interface and deciding the next step. For that kind of work, DeepSeek V4.1 Flash is one of the models we find most interesting.
On Artificial Analysis it has less general capability than GLM-5.3 Flash (39 against 42), but it scores 69% on AutomationBench against 60% for GLM Flash. It is also much faster on the measured infrastructure: about 204 tokens/s against 61.
DeepSeek designed V4.1 Flash around that efficiency. It is a 552B-parameter MoE with 8B active on input and 16B on output, a much smaller KV cache than the previous generation and native visual understanding. If you use Gemini Flash or Luna for agents today, it would be one of the first we would test.
Sources: DeepSeek V4.1 Flash vs GLM-5.3 Flash , DeepSeek: V4.1 Flash .
GPT-6 Luna: here it does make sense to talk about DeepSeek and GLM Flash
Luna should not be compared with the full GLM-5.3. OpenAI designed it for focused, high-volume tasks, so its most direct comparables are DeepSeek V4.1 Flash and GLM-5.3 Flash. The chart shows the comparison well:
Luna still has a very aggressive price on the OpenAI API, and an open model is not automatically cheaper. In this case, beating that price takes open models hosted on your own infrastructure. Even so, you can now compare models in the same category without giving up general capability, and also choose by deployment, infrastructure control or data location.
Sources: Luna vs DeepSeek V4.1 Flash , Luna vs GLM-5.3 Flash .
Volume: Qwen3.8-Flash-Next and Qwen3.6
With millions of requests, the way you choose a model changes. Classification, extraction, data enrichment, moderation, simple RAG or background tasks rarely need your most powerful model. Here we would look for the smallest model that keeps the minimum quality the product needs.
Qwen3.8-Flash-Next would be the first we would test when volume is high but the task still has some complexity: it is a natural comparable for Haiku 5.5 and Luna in batch processing and extraction with some reasoning. Qwen3.6 plays one category lower. Its general capability is considerably lower, so we would not present it as "the open Luna". But when the task is so narrow that paying for more intelligence adds nothing (high-volume RAG, classification, autocomplete, simple extraction or small internal jobs), it is hard to beat on cost. It is a 35B MoE with 3B active parameters and 256K of context.
Here the strategy is the opposite of the frontier tier: start low and move up only if it fails.
Long context: Kimi K3
Kimi stands out in long-context and long-horizon tasks, such as extended coding sessions.
It scores 89% on long-context reasoning against 86% for MiMo and 80% for GLM-5.3. We would test it with huge repositories, document collections, research and long agent sessions. GLM-5.3 would be the choice if that context also needs complex coding; DeepSeek V4.1 Flash, if it lives inside an agent that makes many calls and reuses a lot of information, because its smaller KV cache shows most there.
Vision and multimodality
"Multimodal" is not a box you can tick and consider settled either. Reading a screenshot, understanding a chart and working with audio or video are different problems.
For a fast visual agent we would test DeepSeek V4.1 Flash. For broad multimodal input that also needs a lot of reasoning, MiMo V2.6 Pro accepts text, image, voice and video, and GLM-5.3 covers text, images, video and audio. The closed reference is still Gemini 3.8 Flash, which accepts text, image, video, audio and PDF with 1M of context and built-in tools.
Source: Gemini 3.8 Flash .
If you want to go into detail, our open model guide goes through more than 80 concrete use cases and the model we would pick for each one.
RAG and extraction: do not start with the biggest model
RAG, classification and structured extraction are the workloads where starting with a frontier model makes the least sense. If you have already retrieved the right documents, the model does not need to know everything: it needs to find the evidence, respect the schema and not make things up.
For RAG and extraction we would test, by difficulty:
1. Qwen3.6 for very narrow, high-volume tasks.
2. Qwen3.8-Flash-Next or GLM-5.3 Flash when you need more reasoning or context.
3. DeepSeek V4.1 Flash when extraction is part of an agentic workflow or has a visual component.
4. GLM-5.3 or MiMo V2.6 Pro only when the problem demands it.
Here the overall intelligence index does not decide a winner. We would measure per-field accuracy, schema compliance, faithfulness to context, hallucination rate, retries, latency and cost per correctly solved task. That test has to come from your own data, so the sensible thing is to compare large batches against several models.
When we would still use a closed model
Quite often. If you need the highest level of capability available and cost hardly matters, Opus or Astra are still better options for certain problems. Staying with a vendor also makes sense if you depend heavily on its tools, if volume is small or if you do not yet have a reliable evaluation to measure a migration.
Using open models should not become a standard in itself. If a closed model solves the problem better, use it. The real advantage is being able to decide request by request.
You probably do not need a single model
The architecture that works best for us goes from request → smallest model that can solve it well, instead of application → model.
| Job | Starting point |
|---|---|
| Simple task / batch processing / basic RAG | Qwen3.6 / Qwen3.8-Flash-Next |
| Tools / automation / visual agent | DeepSeek V4.1 Flash |
| Agentic coding, with a good balance | GLM-5.3 Flash |
| Reasoning and knowledge work | MiMo V2.6 Pro |
| Frontier coding and agents | GLM-5.3 |
| Long context | Kimi K3 |
| Exceptional case where a clear gap remains | Opus 5.5 / GPT-6 Astra |
That way the frontier model stops being the one we use by default and becomes the one we escalate to when needed. That is where you really start to notice the change in the economics of an application or an agent.
How to check it with your application
We would take 100, 500 or 1,000 real tasks and run them with the current model and two or three candidates. We would measure quality, success rate, tool calls, retries, latency and cost per completed task. Whenever possible, we would run the evaluation blind.
If your evaluators cannot consistently tell the closed model from the open one, you have a better signal than any overall ranking. If they can, you have learned something too: you know exactly where the gap still is.
Test your workload, not the benchmark
At Helmcode we serve GLM-5.3, GLM-5.3 Flash, DeepSeek V4.1 Flash, Qwen3.6 and other open models behind our OpenAI-compatible API, on European infrastructure and with no log retention. That lets you test several models against the same workload and use the one that makes sense in each part of the product, instead of choosing "the company model".
In six months some of the names in this guide will have changed. The recommendation, however, will stay the same: test your workload, not the benchmark.
