Coding
Directional only- GLM-5.1
- 61.3
- Qwen3.8-Flash-Next
- 62.5
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
GLM-5.1 vs Qwen3.8-Flash-Next
Updated August 26, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 66.99, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
4 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Long documents
Prompts that approach the documented context limit
Qwen3.8-Flash-Next
Qwen3.8-Flash-Next has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Flash-Next has no comparable published API token rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
17 GLM-5.1 only
4 Shared
20 Qwen3.8-Flash-Next only
- Shared results
- 4
- GLM-5.1 only
- 17
- Qwen3.8-Flash-Next only
- 20
- Like-for-like categories
- 0 / 8
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Knowledge
Directional only- GLM-5.1
- 52.3
- Qwen3.8-Flash-Next
- 43.4
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Agentic
Not comparable- GLM-5.1
- 65.4
- Qwen3.8-Flash-Next
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5.1
- Not measured
- Qwen3.8-Flash-Next
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.1
- 62.0
- Qwen3.8-Flash-Next
- Not measured
- Weighted basis
- 4 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.1
- Not measured
- Qwen3.8-Flash-Next
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.1
- Not measured
- Qwen3.8-Flash-Next
- 90.6
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.1
- Not measured
- Qwen3.8-Flash-Next
- 81.3
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | GLM-5.1 | Qwen3.8-Flash-Next | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 61.3 | 62.5 | Directional only2 vs 1 rows | Directional only |
| Knowledge | 52.3 | 43.4 | Directional only1 vs 2 rows | Directional only |
| Agentic | 65.4 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 62.0 | Not measured | Not comparable4 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 90.6 | Not comparable0 vs 1 rows | Not comparable |
| Instruction following | Not measured | 81.3 | Not comparable0 vs 1 rows | Not comparable |
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
HLE
Knowledge
SWE-bench Pro
Coding
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- GLM-5.1
- $0.0036
- Fits in one request
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.8-Flash-Next has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-5.1
- $0.0832
- Fits in one request
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
Qwen3.8-Flash-Next has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-5.1
- $0.352
- Does not fit in one request
- Cached input priced at the published list-input rate
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Flash-Next has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.1Qwen3.8-Flash-Next
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.1
203K
Qwen3.8-Flash-Next
API model ID
GLM-5.1
Not sourced
Qwen3.8-Flash-Next
Qwen/Qwen3.8-Flash-Next
Qwen3.8-Flash-Next model cardCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.1
Not published
Qwen3.8-Flash-Next
No comparable hosted API rate
Qwen3.8-Flash-Next model cardDocumented inputs
GLM-5.1
Not sourced
Qwen3.8-Flash-Next
Not sourced
Documented outputs
GLM-5.1
Not sourced
Qwen3.8-Flash-Next
Not sourced
Provider availability
GLM-5.1
Not sourced
Qwen3.8-Flash-Next
Not sourced
Reasoning profile
GLM-5.1
Reasoning
Qwen3.8-Flash-Next
Reasoning
Weight access
GLM-5.1
Open Weight
Qwen3.8-Flash-Next
Open Weight
License
GLM-5.1
Open Weight
Qwen3.8-Flash-Next
Open Weight
Release date
GLM-5.1
2026-04-07
Qwen3.8-Flash-Next
2026-08-26
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 66.99, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Qwen3.8-Flash-Next has the larger documented window (262K).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
Qwen3.8-Flash-Next
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence41 rows
Agentic
Terminal-Bench 2.0
GLM-5.163.5%SourceQwen3.8-Flash-Next—Not directly comparable
BrowseComp
GLM-5.168%SourceQwen3.8-Flash-Next—Not directly comparable
τ³-bench results
GLM-5.170.6%SourceQwen3.8-Flash-Next—Not directly comparable
MCP Atlas
GLM-5.171.8%SourceQwen3.8-Flash-Next—Not directly comparable
CyberGym
GLM-5.168.7%SourceQwen3.8-Flash-Next—Not directly comparable
Claw-Eval
GLM-5.162.3%SourceQwen3.8-Flash-Next—Not directly comparable
Gert Labs
GLM-5.160.11%SourceQwen3.8-Flash-Next—Not directly comparable
ResearchClawBench
GLM-5.118.2%SourceQwen3.8-Flash-Next—Not directly comparable
CoWorkBench
GLM-5.1—Qwen3.8-Flash-Next73.9%SourceNot directly comparable
JobBench
GLM-5.1—Qwen3.8-Flash-Next55.7%SourceNot directly comparable
Agents' Last Exam
GLM-5.1—Qwen3.8-Flash-Next51.2%SourceNot directly comparable
Toolathlon-Verified
GLM-5.1—Qwen3.8-Flash-Next73.5%SourceNot directly comparable
AndroidWorld
GLM-5.1—Qwen3.8-Flash-Next84.5%SourceNot directly comparable
OSWorld 2.0
GLM-5.1—Qwen3.8-Flash-Next19.4%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.158.4%SourceQwen3.8-Flash-Next62.5%SourceQwen3.8-Flash-Next leads this result
NL2Repo
GLM-5.142.7%SourceQwen3.8-Flash-Next48.1%SourceQwen3.8-Flash-Next leads this result
SWE-Rebench
GLM-5.162.7%SourceQwen3.8-Flash-Next—Not directly comparable
Vibe Code Bench
GLM-5.131.46%SourceQwen3.8-Flash-Next—Not directly comparable
OpenHarmony Bench
GLM-5.152.3%SourceQwen3.8-Flash-Next—Not directly comparable
SWE Multilingual
GLM-5.1—Qwen3.8-Flash-Next81%SourceNot directly comparable
deepSwe
GLM-5.1—Qwen3.8-Flash-Next58.7%SourceNot directly comparable
LiveCodeBench v6
GLM-5.1—Qwen3.8-Flash-Next91.9%SourceNot directly comparable
Knowledge
GPQA-D
GLM-5.186.2%SourceQwen3.8-Flash-Next91.7%SourceQwen3.8-Flash-Next leads this result
HLE
GLM-5.152.3%SourceQwen3.8-Flash-Next35.9%SourceGLM-5.1 leads this result
GPQA
GLM-5.1—Qwen3.8-Flash-Next91.7%SourceNot directly comparable
HLE w/o tools
GLM-5.1—Qwen3.8-Flash-Next35.9%SourceNot directly comparable
Math
AIME26
GLM-5.195.3%SourceQwen3.8-Flash-Next—Not directly comparable
HMMT Nov 2025
GLM-5.194.0%SourceQwen3.8-Flash-Next—Not directly comparable
HMMT Feb 2026
GLM-5.182.6%SourceQwen3.8-Flash-Next—Not directly comparable
MMAnswerBench
GLM-5.183.8%SourceQwen3.8-Flash-Next—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5.133.448%SourceQwen3.8-Flash-Next—Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.112.500%SourceQwen3.8-Flash-Next—Not directly comparable
Multimodal
Vision2Web
GLM-5.1—Qwen3.8-Flash-Next64.0%SourceNot directly comparable
ERQA
GLM-5.1—Qwen3.8-Flash-Next72.3%SourceNot directly comparable
LVBench
GLM-5.1—Qwen3.8-Flash-Next76.6%SourceNot directly comparable
RealWorldQA
GLM-5.1—Qwen3.8-Flash-Next88.5%SourceNot directly comparable
MathVision
GLM-5.1—Qwen3.8-Flash-Next90.6%SourceNot directly comparable
MathVision w/ Python
GLM-5.1—Qwen3.8-Flash-Next95.7%SourceNot directly comparable
CharXiv w/o tools
GLM-5.1—Qwen3.8-Flash-Next84.6%SourceNot directly comparable
CharXiv
GLM-5.1—Qwen3.8-Flash-Next90.6%SourceNot directly comparable
Instruction following
IFBench
GLM-5.1—Qwen3.8-Flash-Next81.3%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5.1 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 66.99, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GLM-5.1 or Qwen3.8-Flash-Next?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GLM-5.1 or Qwen3.8-Flash-Next?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GLM-5.1 or Qwen3.8-Flash-Next?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GLM-5.1 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next has the larger documented context window: 262K, compared with 203K.
Related comparisons
Last updated August 26, 2026
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