Mistral Large 4 vs GLM-5.3: is Europe still in the game?
Mistral's new flagship solved 56 of 148 hard coding problems against 62 for GLM-5.3, China's flagship. The gap is within chance, and Mistral Large 4 wrote seventeen times fewer tokens to get there.
Mistral released Mistral Large 4 on 6 October. We put it beside GLM-5.3, the flagship of China's Z.ai, on 148 hard coding problems, to see whether Europe's best model still keeps up with the best open models from elsewhere.
Published the same day: Germany's Kolibri-1 on a GB10.
The short version
- The score. Mistral Large 4 solved 56 of the 148 problems. GLM-5.3 solved 62 and its smaller sibling GLM-5.3-Flash solved 64. A gap that small is within what chance produces.
- The effort. Mistral Large 4 wrote 0.18 million tokens to answer. GLM-5.3 wrote 3.14 million, seventeen times as many.
- The bill. At list price, the 148 answers cost $0.41 from Mistral Large 4, $14.58 from GLM-5.3 and $1.03 from GLM-5.3-Flash.
- The verdict. Europe is still in the game. Its flagship is level with China's on this test, and an answer from it costs far less.
The three models
- Mistral Large 4. It is French, holds 1.05 trillion parameters and reads 49 billion of them per token. It answers directly unless asked to reason first, and we ran it both ways.
- GLM-5.3. It is Z.ai's flagship, at roughly 750 billion parameters. It reasons before every answer by default.
- GLM-5.3-Flash. It is the smaller sibling, at 320 billion parameters and 18 billion read per token, priced about nine times lower.
The test
We used BigCodeBench-Hard, a set of 148 problems. The model writes a Python function, and the function is run against tests. Each model answered each problem once, with its maker's default settings.
Mistral served Mistral Large 4 itself. Independent hosts served the two GLM models.
What came out
The models mostly agree. Mistral Large 4 and GLM-5.3 gave the same result on 126 of the 148 problems. Mistral solved 8 that GLM-5.3 missed, and GLM-5.3 solved 14 that Mistral missed. Chance alone produces a split like that about three times in ten.
The effort is where they differ. GLM-5.3 reasons at length before it answers, and on some problems it worked through the examples by hand for tens of thousands of tokens. A typical problem took Mistral Large 4 15 seconds and GLM-5.3 about a minute and a half.
Reasoning did not help Mistral Large 4. It solved 56 problems either way, and reasoning cost four times the tokens.
GLM-5.3-Flash did as well as GLM-5.3 for a fourteenth of the cost. On this test, the flagship's extra size bought nothing we could measure.
The test is hard for all of them. 66 of the 148 problems defeated every run, and 42 were solved by every run.
What we cannot tell you
- Whether six problems is a real gap. Settling that would take several times as many problems.
- How the models compare outside coding in English. We did not test French, German, images or long agentic tasks.
- How the GLM models behave on Z.ai's own service. We did not verify how each independent host runs the weights.
- Where an American model would land. We did not run one.
Is Europe still in the game?
On this evidence, yes. France's flagship stands level with China's on hard coding problems, and it gets there in a fraction of the tokens.
That matters to us for a practical reason. CARouter routes to Canadian providers, and open weights are what let a Canadian provider host a model. Mistral describes Mistral Large 4 as open-weight, and its weights were not yet published when we ran this test.