API cost decision in 10 seconds

NewXing4.0 29B A4B (free) vs NewBeam 1.0 501B A23B

Pick Xing4.0 29B A4B (free) for lower cost; pick Beam 1.0 501B A23B only if the larger context window matters more.

Page updated: 2026-10-16T21:07:49Z Data confirmed: 2026-10-16T21:07:49Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Xing4.0 29B A4B (free) for lower cost; pick Beam 1.0 501B A23B only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Xing4.0 29B A4B (free) is estimated at $0 vs $2.5 for Beam 1.0 501B A23B, saving $2.5 (100% lower).

Cost-first pickXing4.0 29B A4B (free)

Context-first pickBeam 1.0 501B A23B

Sample savings$2.5100%

10x traffic gap$25

Beam 1.0 501B A23B has more context, but Xing4.0 29B A4B (free) saves $2.5 on the standard workload. At 10x that traffic, the same price gap is about $25. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Xing4.0 29B A4B (free) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	Xing4.0 29B A4B (free)	Beam 1.0 501B A23B
Input-heavy / RAG	5M input + 500K output	Xing4.0 29B A4B (free)	$0	$6.5
Balanced workload	1M input + 1M output	Xing4.0 29B A4B (free)	$0	$4
Output-heavy chatbot	1M input + 5M output	Xing4.0 29B A4B (free)	$0	$16

Cheaper input Xing4.0 29B A4B (free) $0 vs $1 / 1M

Xing4.0 29B A4B (free) is free for input tokens while Beam 1.0 501B A23B costs $1 per 1M tokens.

Cheaper output Xing4.0 29B A4B (free) $0 vs $3 / 1M

Xing4.0 29B A4B (free) is free for output tokens while Beam 1.0 501B A23B costs $3 per 1M tokens.

Larger context Beam 1.0 501B A23B 262.14K vs 1.05M

Beam 1.0 501B A23B has 786.43K more context (4x larger).

Sample workload Xing4.0 29B A4B (free) $0 vs $2.5

Xing4.0 29B A4B (free) is free for the standard workload while the other model is estimated at $2.5.

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Xing4.0 29B A4B (free) Calculating… Estimated API cost

Beam 1.0 501B A23B 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

Xing4.0 29B A4B (free) has the lower input price; Xing4.0 29B A4B (free) has the lower output price; Beam 1.0 501B A23B offers the larger context window. For the 1M input plus 500K output sample, Xing4.0 29B A4B (free) is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0 for Xing4.0 29B A4B (free) and $2.5 for Beam 1.0 501B A23B.

Best Fit

Choose Xing4.0 29B A4B (free) when you care most about lower input-token price, and lower output-token price.

Choose Beam 1.0 501B A23B when you care most about larger context window.

Decision Notes

On the standard 1M input plus 500K output workload, Xing4.0 29B A4B (free) is estimated at $0 vs $2.5 for Beam 1.0 501B A23B, saving $2.5 (100% lower).
Xing4.0 29B A4B (free) is free for the standard workload while the other model is estimated at $2.5.
Xing4.0 29B A4B (free) is free for input tokens while Beam 1.0 501B A23B costs $1 per 1M tokens.
Xing4.0 29B A4B (free) is free for output tokens while Beam 1.0 501B A23B costs $3 per 1M tokens.
Beam 1.0 501B A23B has 786.43K more context (4x larger).

Head-to-Head Specs

Feature	NewXing4.0 29B A4B (free) (XingChen)	NewBeam 1.0 501B A23B (Reflection)
Input Price prompt tokens per 1M	$0	$1
Completion Price per 1M tokens	$0	$3
Sample Workload Cost 1M input + 500K output	$0	$2.5
Context Window	262.14K	1.05M
Release Date	2026-10-16	2026-10-16

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Xing4.0 29B A4B (free)	On the standard 1M input plus 500K output workload, Xing4.0 29B A4B (free) is estimated at $0 vs $2.5 for Beam 1.0 501B A23B, saving $2.5 (100% lower).
High-volume input processing	Xing4.0 29B A4B (free)	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Xing4.0 29B A4B (free)	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Beam 1.0 501B A23B	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

No lower-cost same-provider swap is currently tracked for this pair.

Larger context near this budget

Grok 4.20 Multi-Agent offers 2M context with $2.5 sample workload cost.
Grok 4.20 offers 2M context with $2.5 sample workload cost.
Llama 4 Scout offers 1.31M context with $0.25 sample workload cost.
MiMo-V2.6-Flash offers 1.05M context with $0.28 sample workload cost.

Popular competitors

DeepSeek V4.1 Flash · DeepSeek · #1
Step 5 Preview · StepFun · #2
GLM 5.3 Flash · Z.ai · #3
MiMo-V2.6-Flash · Xiaomi · #4

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.

Open provider hubs

XingChen catalog

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

Open XingChen models

Reflection catalog

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

Open Reflection models

Xing4.0 29B A4B (free)

Xing4.0-29B-A4B is an open-weight mixture-of-experts language model from China Telecom's AI team (TeleAI) and the successor to the TeleChat series. It has 29B total parameters with 4B active per token...

Beam 1.0 501B A23B

Beam 1.0 is Reflection's open-weight reasoning model for coding and long-horizon agentic workflows. It uses a sparse mixture-of-experts architecture with 501B total and 23B active parameters per token, and a...