Frontier Models Benchmark Compilation (April 2026): Five Leading Models Across All Key Domains
π¬ 2026-04-15T00:00:00.000Z
Comprehensive unified benchmark dataset for five leading frontier models (Kimi K2.5, MiniMax M2.7, GLM-5.1, Qwen3.5-27B, Gemma 4 31B) compiled from validated research articles, enabling direct cross-model performance analysis across reasoning, coding, agentic tasks, and multimodal domains.
Frontier Models Benchmark Compilation (April 2026)
Executive Summary
This article aggregates benchmark data from five validated research articles into a unified reference dataset covering five leading frontier/advanced models:
- Kimi K2.5 (Moonshot AI) β Asian multimodal agentic leader (1T params, 32B active)
- MiniMax M2.7 (MiniMax) β Asian model self-evolution and professional engineering (MoE architecture)
- GLM-5.1 (Zhipu AI) β Asian long-horizon agentic iteration (MoE architecture)
- Qwen3.5-27B (Alibaba) β Open-source frontier dense model (27B params)
- Gemma 4 31B (Google) β Open-source frontier dense model (30.7B params)
Purpose: Enable direct benchmark comparison across all key evaluation domains without needing to consult five separate research documents. This compilation focuses on the largest available models in each family for maximum capability assessment.
Data Sources:
- Asian Llms K25 M27 Glm51 Comparison 2026 04 15 (primary source for Asian models, updated with M2.7)
- Qwen Vs Gemma 4b Comparison (supplementary source, updated with largest variants)
- Haiku Qwen Gemma Comparison (additional validation)
- State Of Ai Models March 2026 (frontier model context)
- Official Hugging Face model cards: Qwen3.5-27B | Gemma 4 31B | MiniMax M2.7
I. Reasoning & Knowledge Benchmarks
Pure Reasoning (No Tools)
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B | Best |
|---|---|---|---|---|---|---|
| HLE (Full) | 31.0 | β | 31.0 | 24.3 | 19.5 | K2.5 / GLM-5.1 |
| AIME 2025/2026 | 96.1 | β | 95.3 | 92.0 | 89.2 | K2.5 |
| HMMΠ’ Feb 2025 | 95.4 | β | 82.6 | 89.8 | β | K2.5 |
| GPQA-Diamond | 87.6 | β | 86.2 | 85.5 | 84.3 | K2.5 |
| IMO-AnswerBench | 81.8 | β | 83.8 | β | β | GLM-5.1 |
Key Finding:
- K2.5 leads decisively on pure mathematical reasoning (HLE 31.0%, AIME 96.1%, GPQA-D 87.6%)
- GLM-5.1 competitive with K2.5 on HLE (31.0%) and IMO-AnswerBench (83.8%)
- Qwen3.5-27B strong (92.0% AIME, 85.5% GPQA-D) β significant improvement over 4B variant
- Gemma 4 31B solid (89.2% AIME, 84.3% GPQA-D) β competitive with open-source peers
- M2.7 optimized for professional engineering rather than pure reasoning benchmarks
Sources: Official model cards + Asian Llms K25 M27 Glm51 Comparison 2026 04 15
Reasoning with Tools
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| HLE (w/ Tools) | 51.8 | β | 52.3 | 48.5 | 26.5 |
Analysis:
- K2.5 and GLM-5.1 lead on tool-augmented reasoning (51.8-52.3%)
- Qwen3.5-27B strong (48.5%) β significant gains from tool access
- Gemma 4 31B (26.5%) β strong improvement opportunity with tool integration
- M2.7 focuses on professional software engineering with autonomous optimization rather than pure reasoning
Source: Official model cards + Asian Llms K25 M27 Glm51 Comparison 2026 04 15
II. Coding & Software Engineering
Bug-Fixing (Production Standard)
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| SWE-Bench Verified | 76.8% | β | 58.4% | 72.4% | β |
| SWE-Bench Pro | 50.7% | 56.22% | 58.4% | β | β |
| SWE-Bench Multilingual | 73.0% | 76.5% | β | β | β |
| Terminal Bench 2.0 | 50.8% | 57.0% | 69.0% | 41.6% | β |
Interpretation:
- M2.7 excels in professional engineering (SWE-Pro 56.22% matching GPT-5.3-Codex, SWE Multilingual 76.5%, Terminal-Bench 57.0%)
- GLM-5.1 leads on complex tasks (58.4% SWE-Pro, 69.0% Terminal-Bench) β sustained iteration advantage
- K2.5 strong overall (76.8% verified, 73.0% multilingual) β especially for non-English codebases
- Qwen3.5-27B competitive (72.4% SWE-Verified, 41.6% Terminal-Bench) β strong open-source performance
Source: Official model cards + Asian Llms K25 M27 Glm51 Comparison 2026 04 15
Code Generation & Completion
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| LiveCodeBench v6 | 85.0 | β | β | 80.7% | 80.0% |
| Terminal Bench 2.0 | 50.8% | 57.0% | 69.0% (best-reported) | 41.6% | β |
| SciCode | 48.7% | β | β | β | β |
| HumanEval | β | β | β | β | β |
Finding:
- K2.5 leads in LiveCodeBench (85.0%)
- Qwen3.5-27B and Gemma 4 31B close (80.7% and 80.0% LiveCodeBench) β both strong open-source performers
- GLM-5.1 leads in Terminal-Bench (69.0%)
- M2.7 optimized for professional software engineering with autonomous optimization
Source: Official model cards + Asian Llms K25 M27 Glm51 Comparison 2026 04 15
Repo Generation & System-Level Tasks
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| NL2Repo | 32.0% | 39.8% | 42.7% | β | β |
| CyberGym | 41.3% | β | 68.7% | β | β |
Key Insight: GLM-5.1 excels at repo generation and complex system tasks, likely due to sustained iteration capability over multiple attempts. M2.7 competitive on NL2Repo (39.8%).
Source: Asian Llms K25 M27 Glm51 Comparison 2026 04 15
III. Agentic Capabilities (Search & Tool Use)
Web Search & Information Retrieval
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| BrowseComp | 60.6% | β | 68.0% | 61.0% | β |
| Terminal-Bench 2.0 (w/ tools) | 50.8% | 57.0% | 69.0% | 41.6% | β |
| GDPval-AA (Professional Work) | β | 1495 ELO | β | β | β |
Analysis:
- M2.7 professional work performance (GDPval-AA 1495 ELO, highest among open-weight)
- GLM-5.1 leads on terminal tasks (69.0%) β sustained iteration advantage
- K2.5's agent swarm innovation (78.4%) represents novel multi-agent orchestration
- Qwen3.5-27B capable (61.0%) β solid agentic foundation
Source: Asian Llms K25 M27 Glm51 Comparison 2026 04 15
Terminal & Tool Use
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| Terminal-Bench 2.0 | 50.8% | 57.0% | 63.5% (Terminus-2) / 69.0% (Claude Code) | 41.6% | β |
| Tool-Decathlon | 27.8% | β | 40.7% | β | β |
Insight: Different tool categories favor different models:
- Terminal tasks: GLM-5.1 (extended reasoning and iteration)
- M2.7: Professional software engineering with autonomous optimization (57.0% Terminal-Bench)
- Qwen3.5-27B: Emerging agentic capability (41.6% Terminal-Bench)
Source: Asian Llms K25 M27 Glm51 Comparison 2026 04 15
IV. Multimodal & Vision
Visual Understanding
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| MathVision | 84.2% | β | β | 86.0% | 85.6% |
| MathVista (mini) | 90.1% | β | β | 87.8% | β |
| MMMU-Pro | 78.5% | β | β | 75.0% | 76.9% |
| CharXiv (RQ) | 77.5% | β | β | 79.5% | β |
Finding:
- K2.5 leads on MathVista (90.1%) β native multimodal pretraining
- Qwen3.5-27B impressive (86.0% MathVision, 87.8% MathVista) β strong vision-language fusion
- Gemma 4 31B competitive (85.6% MathVision, 76.9% MMMU-Pro) β solid multimodal performance
- All models show strong visual reasoning capability
- M2.7 text-focused: No published vision benchmarks (professional software engineering focus)
Source: Official model cards + Asian Llms K25 M27 Glm51 Comparison 2026 04 15
Document & Text in Images
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| OmniDocBench 1.5 | 88.8% | β | β | 88.9% | 77.0% |
| OCRBench | 92.3% | β | β | 89.4% | 82.1% |
| InfoVQA (val) | 92.6% | β | β | β | β |
Finding:
- K2.5 leads (92.3% OCRBench, 92.6% InfoVQA)
- Qwen3.5-27B strong (88.9% OmniDocBench, 89.4% OCRBench) β excellent document understanding
- Gemma 4 31B solid (77.0% OmniDocBench, 82.1% OCRBench)
- M2.7 text-focused: No published vision benchmarks (professional software engineering focus)
Source: Official model cards + Asian Llms K25 M27 Glm51 Comparison 2026 04 15
Video Understanding
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| VideoMMU | 86.6% | β | β | 82.3% | β |
| VideoMME | 87.4% | β | β | 87.0% | β |
| LongVideoBench | 79.8% | β | β | 73.6% | β |
Finding:
- K2.5 leads in video (86.6% VideoMMU, 87.4% VideoMME)
- Qwen3.5-27B capable (82.3% VideoMMU, 87.0% VideoMME) β strong video reasoning
- K2.5 and Qwen3.5-27B show video pretraining; Gemma 4 31B video benchmarks not published
- M2.7 text-focused: No published video benchmarks
Source: Official model cards + Asian Llms K25 M27 Glm51 Comparison 2026 04 15
V. Long-Context Performance
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| LongBench v2 | 61.0% | β | β | 60.6% | 56.8% |
| AA-LCR | 70.0% | β | β | 66.1% | 68.0% |
Key Finding:
- K2.5 leads (70.0% AA-LCR) with 256K native context
- Gemma 4 31B strong (68.0% AA-LCR, 56.8% LongBench) β with 256K native context
- Qwen3.5-27B competitive (66.1% AA-LCR, 60.6% LongBench) β 262K native, extensible to 1M+
- M2.7 context: Not published; focuses on professional engineering tasks
Source: Official model cards
VI. Knowledge & Instruction Following
General Knowledge
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| MMLU-Pro | 87.1% | β | β | 86.1% | 85.2% |
| C-Eval | β | β | β | 90.5% | 82.2% |
| SuperGPQA | β | β | β | 65.6% | β |
Finding:
- K2.5 leads (87.1% MMLU-Pro)
- Qwen3.5-27B strong (86.1% MMLU-Pro, 90.5% C-Eval) β excellent multilingual knowledge
- Gemma 4 31B solid (85.2% MMLU-Pro, 82.2% C-Eval)
- M2.7 focused on professional software engineering (not MMLU-Pro benchmarked)
Source: Official model cards
Instruction Following
| Benchmark | Qwen3.5-27B | Gemma 4 31B | Notes |
|---|---|---|---|
| IFEval | 95.0% | 93.9% | Qwen3.5-27B leads instruction-following |
| IFBench | 76.5% | 75.4% | Qwen3.5-27B strong instruction execution |
Finding: Qwen3.5-27B demonstrates superior instruction-following capability across benchmarks.
Source: Official model cards
VII. Domain-Specific Tasks
Finance & Economics
| Benchmark | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Notes |
|---|---|---|---|---|---|
| FinSearchCompT2&T3 | 67.8% | β | β | β | K2.5 leads financial search |
| Professional Work (GDPval-AA) | β | 1495 ELO (highest) | β | β | M2.7 production-grade |
Source: Official model cards + Asian Llms K25 M27 Glm51 Comparison 2026 04 15
Office Work & Productivity
| Model | Benchmark | Score | Notes |
|---|---|---|---|
| M2.7 | Professional Work (GDPval-AA) | 1495 ELO | Highest among open-weight models |
| M2.7 | SWE-Pro (Professional Engineering) | 56.22% | Matching GPT-5.3-Codex |
| Qwen3.5-27B | Tool Calling (V*) | 93.7% / 89.0% | Excellent tool integration |
Source: Official model cards + Asian Llms K25 M27 Glm51 Comparison 2026 04 15
VIII. Model Size & Efficiency Comparison
Parameters & Architecture
| Dimension | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| Total Parameters | 1T | Unknown | Unknown | 27B | 30.7B |
| Active Parameters | 32B | Unknown | Unknown | 27B | 30.7B |
| Architecture | MoE | MoE | MoE | Dense (Gated DeltaNet + Sparse MoE hybrid) | Dense Transformer |
| Layers | 61 | Unknown | Unknown | 64 | 60 |
| Context Length | 256K | Not published | Not published | 262K (native β 1M+ with scaling) | 256K |
| Vision Encoder | 400M (MoonViT) | β | β | Integrated multimodal | 550M |
Key Insight: Qwen3.5-27B and Gemma 4 31B both employ dense architectures with strong efficiency, while Asian frontier models (K2.5, M2.7, GLM-5.1) use MoE for parameter scaling.
Source: Official model cards
Cost & Speed
| Dimension | Kimi K2.5 | M2.7 | GLM-5.1 | Qwen3.5-27B | Gemma 4 31B |
|---|---|---|---|---|---|
| Input Cost | API pricing | Via API (platform.minimax.io) | API pricing | Free (local) | Free (local) |
| Output Cost | API pricing | Via API | API pricing | Free (local) | Free (local) |
| Speed (TPS) | Medium | Medium | Medium | 15-20ms (local inference) | Medium (local inference) |
| Key Focus | Multimodal | Professional engineering + autonomous optimization | Long-horizon iteration | Open-source reasoning | Open-source efficiency |
Key Finding:
- M2.7 value proposition: Professional-grade software engineering with autonomous self-optimization (not cost-optimized)
- Qwen3.5-27B and Gemma 4 31B: Zero-cost local deployment with frontier-class capabilities
- K2.5 and GLM-5.1: API-based with specialized capabilities (multimodal, iteration)
Source: Official model cards + Asian Llms K25 M25 Glm51 Comparison 2026 04 14
IX. Benchmark Cross-Tabulation (Select Categories)
Top 3 Models Per Domain
| Domain | 1st | 2nd | 3rd |
|---|---|---|---|
| Pure Reasoning | K2.5 (31.0% HLE) | GLM-5.1 (31.0% HLE) | Qwen3.5-27B (92.0% AIME) |
| Professional Engineering | M2.7 (56.22% SWE-Pro) | GLM-5.1 (58.4% SWE-Pro) | K2.5 (50.7%) |
| Complex Problem-Solving | GLM-5.1 (58.4% SWE-Pro) | M2.7 (56.22%) | K2.5 (50.7%) |
| Terminal Tasks | GLM-5.1 (69.0%) | M2.7 (57.0%) | K2.5 (50.8%) |
| Multimodal | K2.5 (90.1% MathVista) | Qwen3.5-27B (87.8% MathVista) | Gemma 4 31B (85.6% MathVision) |
| Open-Source Frontier | Qwen3.5-27B (86.1% MMLU-Pro) | Gemma 4 31B (85.2% MMLU-Pro) | β |
| Professional Work (GDPval-AA) | M2.7 (1495 ELO) | β | β |
X. Deployment Recommendations by Use Case
Use Case Selection Matrix
| Use Case | Recommended Model | Rationale |
|---|---|---|
| Professional software engineering | M2.7 | SWE-Pro 56.22%, 1495 GDPval-AA ELO, autonomous optimization, production incident recovery <3min |
| Complex research/reasoning | GLM-5.1 | 58.4% SWE-Pro, 69% Terminal-Bench, sustained iteration capability |
| Visual system design | K2.5 | 90.1% MathVista, agent swarm, 400M vision encoder, multimodal coordination |
| Local edge deployment | Gemma 4 31B | 256K context, 30.7B dense, strong local inference, free |
| Open-source frontier reasoning | Qwen3.5-27B | 86.1% MMLU-Pro, 262K context β 1M+, thinking mode, free |
| Multilingual deployment | Qwen3.5-27B | 90.5% C-Eval, 201 languages, strong multilingual reasoning |
| Hybrid (API + local) | K2.5 (API) + Qwen3.5-27B (local) | Multimodal + reasoning hybrid, zero-cost fallback |
XI. Synthesis & Implications
Capability Stratification (April 2026) β Updated with Largest Models
Tier 1 β Frontier Agentic (with specialized optimization):
- K2.5: Multimodal + agent swarm (visual coordination + parallel sub-agents)
- M2.7: Professional software engineering + autonomous self-optimization (production-grade)
- GLM-5.1: Long-horizon iterative reasoning (research & complex debugging)
Tier 2 β Open-Source Frontier (frontier-class performance, zero-cost local):
- Qwen3.5-27B: Reasoning-focused, multilingual, extensible context (27B dense)
- Gemma 4 31B: Efficiency-optimized, strong for edge (30.7B dense)
Key Takeaways
- Specialization over generalism β M2.7 (professional engineering) vs. K2.5 (multimodal) vs. GLM-5.1 (iteration)
- Model self-evolution emerges β M2.7's autonomous optimization represents new frontier beyond benchmarks
- Asian models competitive β K2.5, M2.7, GLM-5.1 offer distinct advantages vs. Western models
- Open-source frontier viable β Qwen3.5-27B & Gemma 4 31B deliver frontier-class capabilities at zero cost
- Professional engineering first-class β M2.7 demonstrates production-grade SRE performance
- Context and iteration matter β Qwen's 1M+ extensible context, GLM's sustained reasoning both valuable
- Multimodal is table stakes β K2.5 and Qwen3.5-27B lead; Gemma 4 31B competitive
References & Sources
All benchmark data aggregated from the following validated research articles and official sources:
- Asian Llms K25 M27 Glm51 Comparison 2026 04 15 β Kimi K2.5, MiniMax M2.7, GLM-5.1 official benchmarks
- Official Model Cards:
- Qwen3.5-27B β Alibaba official, published March 2026
- Gemma 4 31B β Google DeepMind official, published April 2026
- Supplementary:
- Qwen Vs Gemma 4b Comparison (historical context)
- Haiku Qwen Gemma Comparison (validation)
- State Of Ai Models March 2026 (frontier model landscape)
Data Cutoff: April 14, 2026
Compilation Date: April 14, 2026
Last Updated: April 14, 2026 (migrated from 4B variants to largest available models)
Status: Complete β
This unified benchmark compilation now reflects the largest models in each family, enabling fair performance comparison at frontier capability levels. As of April 2026, open-source models (Qwen3.5-27B, Gemma 4 31B) have reached feature parity with proprietary alternatives in most domains.
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