API cost decision in 10 seconds

GLM 5V Turbo vs LFM2.5-1.2B-Instruct (free)

Pick LFM2.5-1.2B-Instruct (free) for lower cost; pick GLM 5V Turbo only if the larger context window matters more.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick LFM2.5-1.2B-Instruct (free) for lower cost; pick GLM 5V Turbo only if the larger context window matters more.

On the standard 1M input plus 500K output workload, LFM2.5-1.2B-Instruct (free) is estimated at $0 vs $3.2 for GLM 5V Turbo, saving $3.2 (100% lower).

Cost-first pickLFM2.5-1.2B-Instruct (free)
Context-first pickGLM 5V Turbo
Sample savings$3.2100%
10x traffic gap$32

GLM 5V Turbo has more context, but LFM2.5-1.2B-Instruct (free) saves $3.2 on the standard workload. At 10x that traffic, the same price gap is about $32. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

LFM2.5-1.2B-Instruct (free) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickGLM 5V TurboLFM2.5-1.2B-Instruct (free)
Input-heavy / RAG5M input + 500K outputLFM2.5-1.2B-Instruct (free)$8$0
Balanced workload1M input + 1M outputLFM2.5-1.2B-Instruct (free)$5.2$0
Output-heavy chatbot1M input + 5M outputLFM2.5-1.2B-Instruct (free)$21.2$0
Cheaper input LFM2.5-1.2B-Instruct (free) $1.2 vs $0 / 1M

LFM2.5-1.2B-Instruct (free) is free for input tokens while GLM 5V Turbo costs $1.2 per 1M tokens.

Cheaper output LFM2.5-1.2B-Instruct (free) $4 vs $0 / 1M

LFM2.5-1.2B-Instruct (free) is free for output tokens while GLM 5V Turbo costs $4 per 1M tokens.

Larger context GLM 5V Turbo 202.75K vs 32.77K

GLM 5V Turbo has 169.98K more context (6.19x larger).

Sample workload LFM2.5-1.2B-Instruct (free) $3.2 vs $0

LFM2.5-1.2B-Instruct (free) is free for the standard workload while the other model is estimated at $3.2.

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
GLM 5V Turbo Calculating… Estimated API cost
LFM2.5-1.2B-Instruct (free) Calculating… Estimated API cost
Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

LFM2.5-1.2B-Instruct (free) has the lower input price; LFM2.5-1.2B-Instruct (free) has the lower output price; GLM 5V Turbo offers the larger context window. For the 1M input plus 500K output sample, LFM2.5-1.2B-Instruct (free) is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $3.2 for GLM 5V Turbo and $0 for LFM2.5-1.2B-Instruct (free).

Best Fit

Choose GLM 5V Turbo when you care most about larger context window.

Choose LFM2.5-1.2B-Instruct (free) when you care most about lower input-token price, and lower output-token price.

Decision Notes
  • On the standard 1M input plus 500K output workload, LFM2.5-1.2B-Instruct (free) is estimated at $0 vs $3.2 for GLM 5V Turbo, saving $3.2 (100% lower).
  • LFM2.5-1.2B-Instruct (free) is free for the standard workload while the other model is estimated at $3.2.
  • LFM2.5-1.2B-Instruct (free) is free for input tokens while GLM 5V Turbo costs $1.2 per 1M tokens.
  • LFM2.5-1.2B-Instruct (free) is free for output tokens while GLM 5V Turbo costs $4 per 1M tokens.
  • GLM 5V Turbo has 169.98K more context (6.19x larger).
Head-to-Head Specs
FeatureGLM 5V Turbo
(Z.ai)
LFM2.5-1.2B-Instruct (free)
(LiquidAI)
Input Price
prompt tokens per 1M
$1.2$0
Completion Price
per 1M tokens
$4$0
Sample Workload Cost
1M input + 500K output
$3.2$0
Context Window202.75K32.77K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionLFM2.5-1.2B-Instruct (free)On the standard 1M input plus 500K output workload, LFM2.5-1.2B-Instruct (free) is estimated at $0 vs $3.2 for GLM 5V Turbo, saving $3.2 (100% lower).
High-volume input processingLFM2.5-1.2B-Instruct (free)Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsLFM2.5-1.2B-Instruct (free)Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workGLM 5V TurboA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps
  • GLM 4.5 Air (free) can replace GLM 5V Turbo when lower sample workload cost matters most: $0.
  • GLM 4 32B can replace GLM 5V Turbo when lower sample workload cost matters most: $0.15.
  • GLM 4.7 Flash can replace GLM 5V Turbo when lower sample workload cost matters most: $0.26.
  • GLM 4.5 Air can replace GLM 5V Turbo when lower sample workload cost matters most: $0.55.
Larger context near this budget
  • Llama 4 Scout offers 10M context with $0.23 sample workload cost.
  • Grok 4.20 offers 2M context with $2.5 sample workload cost.
  • Owl Alpha offers 1.05M context with $0 sample workload cost.
  • DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

Open cheapest models

Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

Open largest context models

Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

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Z.ai catalog

Review all tracked Z.ai models before deciding whether this matchup is the right shortlist.

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LiquidAI catalog

Check other LiquidAI models with comparable pricing, context, or release timing.

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GLM 5V Turbo

GLM-5V-Turbo is Z.ai’s first native multimodal agent foundation model, built for vision-based coding and agent-driven tasks. It natively handles image, video, and text inputs, excels at long-horizon planning, complex coding,...

LFM2.5-1.2B-Instruct (free)

LFM2.5-1.2B-Instruct is a compact, high-performance instruction-tuned model built for fast on-device AI. It delivers strong chat quality in a 1.2B parameter footprint, with efficient edge inference and broad runtime support.