Head-to-head · multimodal vs multimodal

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

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

Anthropic

Claude Haiku 4.5 Turbo

Claude Haiku 4.5 Turbo is a multimodal model from Anthropic. It offers a 200,000-token context window, costs $14.37 per million input tokens and $54.76 per million output tokens, and averages 80.0 across the index's benchmark suite.

Input
$14.37 / 1M
Output
$54.76 / 1M
Context
200,000
Avg score
80.0
Open weights
No
Full Claude Haiku 4.5 Turbo page
Zhipu AI

GLM-4.6

GLM-4.6 is a multimodal model from Zhipu AI. It offers a 128,000-token context window, costs $5.10 per million input tokens and $18.45 per million output tokens, and averages 80.2 across the index's benchmark suite.

Input
$5.10 / 1M
Output
$18.45 / 1M
Context
128,000
Avg score
80.2
Open weights
Yes
Full GLM-4.6 page

Specification comparison

SpecClaude Haiku 4.5 TurboGLM-4.6Edge
Input price / 1M$14.37$5.10B
Output price / 1M$54.76$18.45B
Context window200,000128,000A
Max output32,7688,192A
Average score80.080.2B
Arena Elo1434.51440.7B
Open weightsNoYesB

Benchmark comparison

Claude Haiku 4.5 Turbo wins 6, GLM-4.6 wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkClaude Haiku 4.5 TurboGLM-4.6GapWinner
MMLU-Pro80.684.03.4GLM-4.6
GPQA Diamond75.178.33.2GLM-4.6
SWE-bench Verified68.467.41.0Claude Haiku 4.5 Turbo
HumanEval87.083.83.2Claude Haiku 4.5 Turbo
MATH-50083.783.40.3Claude Haiku 4.5 Turbo
AIME 202579.280.21.0GLM-4.6
LiveCodeBench71.771.60.1Claude Haiku 4.5 Turbo
IFEval87.186.40.7Claude Haiku 4.5 Turbo
TruthfulQA81.082.51.5GLM-4.6
HellaSwag88.189.41.3GLM-4.6
MMMU77.775.42.3Claude Haiku 4.5 Turbo
Arena Elo1434.51440.76.2GLM-4.6

Price analysis

On a blended 3:1 input-to-output basis, Claude Haiku 4.5 Turbo costs about $24.47 per million tokens and GLM-4.6 costs about $8.44. GLM-4.6 is roughly 66% cheaper than Claude Haiku 4.5 Turbo. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

Claude Haiku 4.5 Turbo has the larger context window at 200,000 tokens, compared with 128,000 for GLM-4.6. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, Claude Haiku 4.5 Turbo wins 6, GLM-4.6 wins 6, and 0 are tied. On the index average, GLM-4.6 leads Claude Haiku 4.5 Turbo by 0.2 points (80.2 versus 80.0).

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

Choose Claude Haiku 4.5 Turbo if

  • You need the larger 200,000-token context window.
  • You need the larger 32,768-token output limit.

Choose GLM-4.6 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 Turbo better than GLM-4.6?

On the index average, GLM-4.6 leads Claude Haiku 4.5 Turbo by 0.2 points (80.2 versus 80.0). The better choice depends on price, context and your task. Claude Haiku 4.5 Turbo wins 6 benchmarks, GLM-4.6 wins 6.

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

GLM-4.6 is cheaper on a blended 3:1 basis at about $8.44 per million tokens, versus $24.47 for Claude Haiku 4.5 Turbo.

Which has the larger context window?

Claude Haiku 4.5 Turbo has the larger context window at 200,000 tokens. GLM-4.6 has 128,000 tokens.

Are these models open weights?

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

Which should I choose for production?

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

Related comparisons