Updated November 5, 2026. Rank says GLM-5.2 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GLM-5.2 has the higher public point estimate, 60.23 versus 57.27. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
MiMo-V2.6-Pro
MiMo-V2.6-Pro has the higher public coding point estimate, 57.1 to 53.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
MiMo-V2.6-Pro
MiMo-V2.6-Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
MiMo-V2.6-Pro
MiMo-V2.6-Pro has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
50K fresh input + 3K output tokens
MiMo-V2.6-Pro
MiMo-V2.6-Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MiMo-V2.6-Pro is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
MiMo-V2.6-Pro has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | GLM-5.2 | MiMo-V2.6-Pro | Basis | Reading |
|---|---|---|---|---|
| Coding | 53.6Supported · #35/156 | 57.1Supported · #25/156 | Like-for-likeBenchAlign v5.8 lane · 9 vs 4 public rows | MiMo-V2.6-Pro leads · intervals overlap |
| Knowledge | 57.0Supported · #55/177 | 62.3Supported · #42/177 | Like-for-likeBenchAlign v5.8 lane · 6 vs 0 public rows | MiMo-V2.6-Pro leads · intervals overlap |
| Agentic | 56.9Supported · #39/135 | 56.6Estimated · #40/135 | Directional onlyBenchAlign v5.8 lane · 8 vs 9 public rows | Directional only |
| Reasoning | 78.8Unranked · 2 rankable rows | 82.5Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 89.9#29/157 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 78.4#3/12 | Not ranked | Not comparableProvisional lane · 4 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
1K fresh input + 500 output tokens
MiMo-V2.6-Pro has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiMo-V2.6-Pro has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
MiMo-V2.6-Pro has the lower modeled cost
GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
MiMo-V2.6-Pro
GLM-5.2
Not sourced
MiMo-V2.6-Pro
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.2
Not published
MiMo-V2.6-Pro
$0.0036 per 1M cached input tokens
Xiaomi MiMo-V2.6 launchGLM-5.2
Not sourced
MiMo-V2.6-Pro
Not sourced
GLM-5.2
Not sourced
MiMo-V2.6-Pro
Not sourced
GLM-5.2
Not sourced
MiMo-V2.6-Pro
Not sourced
GLM-5.2
Reasoning
MiMo-V2.6-Pro
Reasoning
GLM-5.2
Open Weight
MiMo-V2.6-Pro
Open Weight
GLM-5.2
Open Weight
MiMo-V2.6-Pro
Open Weight
GLM-5.2
2026-06-16
MiMo-V2.6-Pro
2026-09-22
GLM-5.2 has the higher public point estimate, 60.23 versus 57.27. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
MiMo-V2.6-Pro has the higher public coding point estimate, 57.1 to 53.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GLM-5.2 scores higher for agentic tasks on the public lane, 56.9 to 56.6. MiMo-V2.6-Pro is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.00087 on MiMo-V2.6-Pro; repository review costs $0.0832 and $0.02436; the cache-heavy agent loop costs $0.352 and $0.01812. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Both models list the same context window, 1M.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.1
MiMo-V2.6-Pro leads this result
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
HLE w/ tools
Not directly comparable
MazeBench
Not directly comparable
Toolathlon-Verified
Not directly comparable
AutomationBench
Not directly comparable
Agents' Last Exam
Not directly comparable
Terminal-Bench 4.0
Not directly comparable
OSWorld-Verified
Not directly comparable
JobBench
Not directly comparable
CyberGym
Not directly comparable
ExploitGym
Not directly comparable
SWE-bench Pro
Not directly comparable
NL2Repo
Not directly comparable
Terminal-Bench 2.1
MiMo-V2.6-Pro leads this result
ProgramBench
GLM-5.2 leads this result
CursorBench 3.2
Not directly comparable
OpenHarmony Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
PostTrainBench v1.1
Not directly comparable
DeepSWE
Not directly comparable
Bug Hunt Bench
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 6,100+ subscribers.
Last updated November 5, 2026
