As of August 20, 2026, GLM-4.5 is 77% cheaper per million tokens than Grok-2. GLM-4.5 is $0.60 / $2.20 per million input/output tokens versus Grok-2 at $2.00 / $10.00. Sources: https://docs.z.ai/guides/overview/pricing and https://docs.x.ai/docs/models
Sources: Z AI pricing (https://docs.z.ai/guides/overview/pricing); xAI API pricing (https://docs.x.ai/docs/models)
GLM-4.5
Z AI
Input
$0.60
Output
$2.20
Grok-2
xAI
Input
$2.00
Output
$10.00
Token pricing, specs, and benchmarks side by side
GLM-4.5Grok-2Verdict
GLM-4.5 offers both lower pricing and stronger benchmark performance across the board, making it the clear value leader in this comparison.
As of August 20, 2026, GLM-4.5 is 77% cheaper on blended LLM API pricing. GLM-4.5 costs $0.60 per million input tokens and $2.20 per million output tokens ($2.80 blended 1M-in + 1M-out). Grok-2 costs $2.00 / $10.00 per million tokens ($12.00 blended). Sources: https://docs.z.ai/guides/overview/pricing and https://docs.x.ai/docs/models. The cheapest LLM for your app still depends on how many output tokens you generate.
GLM-4.5 Z AI API pricing is $0.60 input and $2.20 output per million tokens via Z AI. Grok-2 xAI API pricing is $2.00 input and $10.00 output per million tokens via xAI. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
GLM-4.5 has a published SWE-bench Verified score of 64.2%. Grok-2 does not have that eval on this page yet.
GLM-4.5 supports a 131K token context window, while Grok-2 supports 128K tokens. GLM-4.5 offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
GLM-4.5 leads on 3 of 4 shared benchmarks versus Grok-2's 1 wins. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Both GLM-4.5 and Grok-2 are production API models. For cost-sensitive production traffic, GLM-4.5 has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Yes. Call GLM-4.5 through Z AI and Grok-2 through xAI with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
List rates are the latest published Z AI API pricing and xAI API pricing, quoted per million tokens. Official rate sheets: https://docs.z.ai/guides/overview/pricing and https://docs.x.ai/docs/models. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
GLM-4.5 supports up to 131K output tokens per request, while Grok-2 supports up to 8K output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
GLM-4.5 runs at approximately 52 tokens/second while Grok-2 runs at 85 tokens/second. Grok-2 is faster in raw throughput. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open GLM-4.5 or Grok-2 from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
This comparison covers API pricing, context window, throughput, and benchmark scores. GLM-4.5 costs $0.60/M input and $2.20/M output. Grok-2 costs $2.00/M input and $10.00/M output.
Rank both on the LLM leaderboard, SWE-bench, and Humanity's Last Exam.
The index
280 models across 33 providers. Search or jump to a lab — every model page stays linked here.
Next step
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