Head-to-head · multimodal vs multimodal

Claude Haiku 4.5 vs GLM-4.6 Chat: which model should you choose?

We compared Claude Haiku 4.5 and GLM-4.6 Chat on price, context window and 12 benchmarks. GLM-4.6 Chat has the stronger average benchmark score; GLM-4.6 Chat is cheaper on a blended 3:1 basis.

Anthropic

Claude Haiku 4.5

Claude Haiku 4.5 is a multimodal model from Anthropic. It offers a 1,000,000-token context window, costs $13.86 per million input tokens and $74.67 per million output tokens, and averages 79.4 across the index's benchmark suite.

Input
$13.86 / 1M
Output
$74.67 / 1M
Context
1,000,000
Avg score
79.4
Open weights
No
Full Claude Haiku 4.5 page
Zhipu AI

GLM-4.6 Chat

GLM-4.6 Chat is a multimodal model from Zhipu AI. It offers a 400,000-token context window, costs $4.89 per million input tokens and $21.46 per million output tokens, and averages 79.8 across the index's benchmark suite.

Input
$4.89 / 1M
Output
$21.46 / 1M
Context
400,000
Avg score
79.8
Open weights
Yes
Full GLM-4.6 Chat page

Specification comparison

SpecClaude Haiku 4.5GLM-4.6 ChatEdge
Input price / 1M$13.86$4.89B
Output price / 1M$74.67$21.46B
Context window1,000,000400,000A
Max output128,00016,384A
Average score79.479.8B
Arena Elo1421.81437.7B
Open weightsNoYesB

Benchmark comparison

Claude Haiku 4.5 wins 5, GLM-4.6 Chat wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkClaude Haiku 4.5GLM-4.6 ChatGapWinner
MMLU-Pro84.182.21.9Claude Haiku 4.5
GPQA Diamond74.172.81.3Claude Haiku 4.5
SWE-bench Verified69.266.03.2Claude Haiku 4.5
HumanEval84.786.31.6GLM-4.6 Chat
MATH-50081.486.85.4GLM-4.6 Chat
AIME 202576.579.22.7GLM-4.6 Chat
LiveCodeBench71.669.62.0Claude Haiku 4.5
IFEval85.287.82.6GLM-4.6 Chat
TruthfulQA79.683.23.6GLM-4.6 Chat
HellaSwag89.689.90.3GLM-4.6 Chat
MMMU77.673.54.1Claude Haiku 4.5
Arena Elo1421.81437.715.9GLM-4.6 Chat

Price analysis

On a blended 3:1 input-to-output basis, Claude Haiku 4.5 costs about $29.06 per million tokens and GLM-4.6 Chat costs about $9.03. GLM-4.6 Chat is roughly 69% cheaper than Claude Haiku 4.5. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

Claude Haiku 4.5 has the larger context window at 1,000,000 tokens, compared with 400,000 for GLM-4.6 Chat. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, Claude Haiku 4.5 wins 5, GLM-4.6 Chat wins 7, and 0 are tied. On the index average, GLM-4.6 Chat leads Claude Haiku 4.5 by 0.4 points (79.8 versus 79.4).

The price picture favours GLM-4.6 Chat. The context picture favours Claude Haiku 4.5. 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 GLM-4.6 Chat for quality-sensitive work, and check whether its price holds at your expected volume before committing.

Choose Claude Haiku 4.5 if

  • You need the larger 1,000,000-token context window.
  • You need the larger 128,000-token output limit.

Choose GLM-4.6 Chat if

  • You want the higher average benchmark score in this index.
  • Unit cost matters and you want the cheaper blended price.
  • You need open weights for self-hosting or fine-tuning.

FAQ

Is Claude Haiku 4.5 better than GLM-4.6 Chat?

On the index average, GLM-4.6 Chat leads Claude Haiku 4.5 by 0.4 points (79.8 versus 79.4). The better choice depends on price, context and your task. Claude Haiku 4.5 wins 5 benchmarks, GLM-4.6 Chat wins 7.

Which is cheaper, Claude Haiku 4.5 or GLM-4.6 Chat?

GLM-4.6 Chat is cheaper on a blended 3:1 basis at about $9.03 per million tokens, versus $29.06 for Claude Haiku 4.5.

Which has the larger context window?

Claude Haiku 4.5 has the larger context window at 1,000,000 tokens. GLM-4.6 Chat has 400,000 tokens.

Are these models open weights?

Claude Haiku 4.5 is proprietary; GLM-4.6 Chat is open weights.

Which should I choose for production?

If you need a single default, start with GLM-4.6 Chat for quality-sensitive work, and check whether its price holds at your expected volume before committing.

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