Head-to-head · multimodal vs multimodal

GLM-4.6 Instruct vs Nova Pro Max: which model should you choose?

We compared GLM-4.6 Instruct and Nova Pro Max on price, context window and 12 benchmarks. Nova Pro Max has the stronger average benchmark score; GLM-4.6 Instruct is cheaper on a blended 3:1 basis.

Zhipu AI

GLM-4.6 Instruct

GLM-4.6 Instruct is a multimodal model from Zhipu AI. It offers a 1,000,000-token context window, costs $5.15 per million input tokens and $24.32 per million output tokens, and averages 80.4 across the index's benchmark suite.

Input
$5.15 / 1M
Output
$24.32 / 1M
Context
1,000,000
Avg score
80.4
Open weights
Yes
Full GLM-4.6 Instruct page
Amazon

Nova Pro Max

Nova Pro Max is a multimodal model from Amazon. It offers a 1,000,000-token context window, costs $14.05 per million input tokens and $71.59 per million output tokens, and averages 81.2 across the index's benchmark suite.

Input
$14.05 / 1M
Output
$71.59 / 1M
Context
1,000,000
Avg score
81.2
Open weights
No
Full Nova Pro Max page

Specification comparison

SpecGLM-4.6 InstructNova Pro MaxEdge
Input price / 1M$5.15$14.05A
Output price / 1M$24.32$71.59A
Context window1,000,0001,000,000Tie
Max output128,00016,384A
Average score80.481.2B
Arena Elo1426.61423.2A
Open weightsYesNoA

Benchmark comparison

GLM-4.6 Instruct wins 7, Nova Pro Max wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkGLM-4.6 InstructNova Pro MaxGapWinner
MMLU-Pro80.684.23.6Nova Pro Max
GPQA Diamond75.878.32.5Nova Pro Max
SWE-bench Verified70.567.43.1GLM-4.6 Instruct
HumanEval85.387.52.2Nova Pro Max
MATH-50084.983.81.1GLM-4.6 Instruct
AIME 202574.579.34.8Nova Pro Max
LiveCodeBench72.677.04.4Nova Pro Max
IFEval87.486.41.0GLM-4.6 Instruct
TruthfulQA83.883.40.4GLM-4.6 Instruct
HellaSwag90.688.91.7GLM-4.6 Instruct
MMMU78.977.21.7GLM-4.6 Instruct
Arena Elo1426.61423.23.4GLM-4.6 Instruct

Price analysis

On a blended 3:1 input-to-output basis, GLM-4.6 Instruct costs about $9.94 per million tokens and Nova Pro Max costs about $28.44. GLM-4.6 Instruct is roughly 65% cheaper than Nova Pro Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

Both models list a 1,000,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, GLM-4.6 Instruct wins 7, Nova Pro Max wins 5, and 0 are tied. On the index average, Nova Pro Max leads GLM-4.6 Instruct by 0.8 points (81.2 versus 80.4).

The price picture favours GLM-4.6 Instruct. The context picture is effectively a tie. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.

If you need a single default, start with Nova Pro Max for quality-sensitive work and GLM-4.6 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Choose GLM-4.6 Instruct if

  • Unit cost matters and you want the cheaper blended price.
  • You need the larger 128,000-token output limit.
  • You need open weights for self-hosting or fine-tuning.

Choose Nova Pro Max if

  • You want the higher average benchmark score in this index.

FAQ

Is GLM-4.6 Instruct better than Nova Pro Max?

On the index average, Nova Pro Max leads GLM-4.6 Instruct by 0.8 points (81.2 versus 80.4). The better choice depends on price, context and your task. GLM-4.6 Instruct wins 7 benchmarks, Nova Pro Max wins 5.

Which is cheaper, GLM-4.6 Instruct or Nova Pro Max?

GLM-4.6 Instruct is cheaper on a blended 3:1 basis at about $9.94 per million tokens, versus $28.44 for Nova Pro Max.

Which has the larger context window?

Both models list a 1,000,000-token context window.

Are these models open weights?

GLM-4.6 Instruct is open weights; Nova Pro Max is proprietary.

Which should I choose for production?

If you need a single default, start with Nova Pro Max for quality-sensitive work and GLM-4.6 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

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