GLM-5 vs Kimi K2.5 vs MiniMax M2.5: a practical coding showdown
Three mixture-of-experts models launched within three weeks of each other — Kimi K2.5 on January 27, GLM-5 on February 11, MiniMax M2.5 on February 12 — and all three claim to rival Claude Opus 4.6 at coding tasks. At 5–20× lower cost.
I’ve been tracking developer reactions, running these through Claude Code and Kilo CLI, and reading every comparison I can find. The short version: they’re all genuinely good, but they’re good at different things, and the benchmarks don’t tell you which one to actually use.
The specs at a glance
| GLM-5 (Z.ai) | Kimi K2.5 (Moonshot) | MiniMax M2.5 | |
|---|---|---|---|
| Total / active params | 744B / 40B | 1.04T / 32B | 230B / 10B |
| Context window | 200K | 256K | 205K |
| SWE-Bench Verified | 77.8% | 76.8% | 80.2% |
| Terminal-Bench 2.0 | 49.4% | 50.8% | 52.0% |
| Hallucination (AA) | -1 (best) | -11 (worst) | 36th percentile |
| Multimodal | Text only | Vision + text | Text only |
| Licence | MIT | Modified MIT | Modified MIT |
| Output per 1M tokens | $3.20 | $3.00 | $1.20 |
For reference, Opus 4.6 scores 80.8% on SWE-Bench Verified and 65.4% on Terminal-Bench 2.0. That Terminal-Bench gap — 9+ points over all three Chinese models — matters more than SWE-Bench for anyone running long agentic sessions. It measures real autonomous terminal operations, not just patch generation.
MiniMax M2.5 sits 0.6 points behind Opus on SWE-Bench at roughly one-twentieth the output cost. That’s the number that got everyone’s attention.
What developers are actually saying
The benchmark story is one thing. What people report after a week of real usage is another.
On MiniMax M2.5, Thomas Wiegold (AI consultant) wrote on February 14: “MiniMax’s M2.5 model is the first time I’ve genuinely questioned whether my Claude Max subscription is worth it… M2.5 gave me the best result I’ve gotten so far. Better than Claude Code with Opus 4.6.” Developer @himanshustwts posted to X (46.8K views): “I have been vibetesting Minimax M2.5 on CC since a week. Its a wild model like an ultimate agentic workhorse.”
On GLM-5, the same @himanshustwts (198K views) called it “crazy model… impressively good in design (one shotted better UI with GLM than Opus 4.6)… nearly no hallucination.” A Towards AI blogger cancelled their Claude subscription “four hours after downloading GLM-5.” Bold move.
On Kimi K2.5, the tension is captured perfectly by a Quickleap developer: “Kimi gives you something. Claude gives you the right thing.” After testing a race condition: “Kimi identified the async issue but suggested adding a random delay. That’s not a fix. That’s a band-aid.” Claude traced the execution flow and suggested proper locking. Multiple users also discovered K2.5 sometimes identifies itself as Claude — a strong hint about training data provenance.
The most grounded head-to-head comes from Kilo Code’s blog (February 25), which tested GLM-5 and M2.5 across three autonomous TypeScript tasks: bug hunting in a Hono/Prisma API, legacy refactoring, and building an API from an OpenAPI spec. GLM-5 scored 90.5/100 with superior architecture and testing quality but took 44 minutes. M2.5 scored 88.5/100 with better instruction adherence and finished in 21 minutes. Both found all eight planted bugs. Half the time for 2 fewer points? That’s a trade most developers would take.
How each performs in agentic tools
For anyone embedded in Claude Code, integration quality is make-or-break. All three work via ANTHROPIC_BASE_URL and ANTHROPIC_AUTH_TOKEN environment variables, but the experience varies considerably.
MiniMax M2.5 gets the strongest endorsement for agentic work. OpenHands ranked it 4th overall behind only Opus-family models and GPT-5.2 Codex, calling it “the first open model that has exceeded Claude Sonnet.” Verdent.ai’s seven-day production test recommended: “Use M2.5 as your default agent model for iterative, tool-heavy, multi-file coding work. Route to Opus 4.6 when a mistake has significant downstream consequences.”
But it’s not flawless. Mike Slinn’s extensive testing documented M2.5 secretly changing linter thresholds rather than fixing code — bumping a gocognit max-complexity from 15 to 70 to make errors disappear. He called it “lazy and deceitful.” That’s the kind of behaviour you won’t catch without careful code review.
GLM-5’s Achilles heel is sequential processing. John Wong’s detailed comparison found that where Opus fires off parallel file reads, lint, and typecheck simultaneously, GLM-5 does everything one at a time. “Read a file. Wait. Read another file. Wait.” The throughness is impressive — it proactively handles unanswered questions, adds pagination, defines constants — but wall-clock time roughly doubles. Wong’s verdict after configuring the three-tier mapping (GLM-5→Opus, GLM-4.7→Sonnet, GLM-4.5-Air→Haiku): “Implementation speed moved from painful to workable.”
Kimi K2.5 has its own native agent, Kimi Code CLI (6,400+ GitHub stars), which integrates with VS Code, Cursor, and Zed. Its Agent Swarm technology coordinates up to 100 parallel sub-agents with 1,500 tool calls. Sounds impressive on paper. The catch: K2.5 generates 6× more tokens per task than the median model (89M output tokens where typical is 14M), which dilutes the speed advantage and inflates costs. Composio’s testing found it “genuinely a good model, roughly 8–9× cheaper than Opus 4.5” but added “I still prefer Claude Opus 4.5 for actual software work.”
Task-by-task breakdown
Aggregate benchmarks hide the task-specific differences that actually matter for daily work.
Backend and architecture
GLM-5 and M2.5 both shine here, but differently. GLM-5 “thinks like a thorough implementer” (Wong) — it proactively covers edge cases, adds data retention policies, writes different modal copy for positive versus negative feedback. M2.5 exhibits what MiniMax calls an “Architect Mindset” that emerged from RL training: before writing code, it decomposes and plans features, structure, and API design.
For API work, M2.5’s spec-first approach produces cleaner initial scaffolding. GLM-5 is more thorough on implementation details. Opus 4.6 remains the gold standard for cross-codebase reasoning — developers on Reddit note all three Chinese models “treat each file independently” while “Opus can maintain a mental model of the entire project.”
Frontend and UI
Kimi K2.5’s territory. Its native vision enables uploading screenshots or screen recordings and generating functional HTML/CSS/JS, including scroll-triggered animations. It can render its own UI output, compare it to the original design, spot CSS discrepancies, and fix them autonomously. That visual debugging loop is genuinely useful for component-heavy work.
GLM-5 surprised people — @himanshustwts reported it “one shotted better UI with GLM than Opus 4.6” — but it can’t process visual inputs. M2.5 is weakest here: 302.AI noted it “feels rigid when handling vague requirements or aesthetic judgements… it can build a structurally sound wooden chair, but if you ask it to carve beautiful patterns, it scratches its head.”
Long autonomous runs
Opus 4.6’s decisive advantage. It holds “the longest task-completion time horizon of any model evaluated by METR” and that Terminal-Bench lead of 9+ points reflects genuine superiority in autonomous terminal operations.
M2.5 is the best challenger — matching Opus’s average SWE-Bench task time (22.8 vs 22.9 minutes) and performing “particularly well at long-running tasks of developing new apps from scratch” (OpenHands). GLM-5’s long-horizon benchmarks are strong (#1 open-source on Vending Bench 2) but serial tool execution means roughly 2× slower wall-clock time. Kimi K2.5’s 6× token verbosity makes long sessions expensive fast.
Ambiguous specs
Kimi K2.5 “distinguishes itself through superior natural language understanding — developers note it better interprets ambiguous prompts with fewer follow-up clarifications.” GLM-5 proactively answers unanswered questions — Wong found it added a concrete data retention policy where Opus ignored the ambiguity. M2.5 is weakest on ambiguity, tending to “plow ahead indiscriminately, wreaking chaos” (Slinn) rather than asking for clarification.
Speed
MiniMax M2.5-Lightning runs at 100 tokens/second — the fastest frontier-class coding model. GLM-5 outputs at ~68 tok/s but sequential tool execution roughly doubles effective task time. Kimi K2.5 starts fast but generates 6× more tokens per task than average, making it feel slower despite decent per-token throughput.
Hallucination and reliability
This is where the three diverge most, and it matters enormously for production codebases.
GLM-5 has the lowest hallucination rate of any frontier-class model. Z.ai claims a 56 percentage-point reduction versus GLM-4.7, corroborated by Artificial Analysis’s AA-Omniscience Index score of -1 (a 35-point improvement over its predecessor). VentureBeat confirmed the “record low hallucination rate.” The caveat: WaveSpeedAI found it “introduced more selective confident errors — statements that read authoritative but were wrong about niche facts.” So it hallucinates less, but when it does, it’s harder to catch.
Kimi K2.5 is a serious concern. Its AA-Omniscience score of -11 (where Claude Opus 4.5 scores +10) means it produces more incorrect than correct answers on factual benchmarks. Maxime Labonne noted: “That hallucination gap is the bit most people gloss over. In practice I’ve found K2.5 brilliant for multi-step coding but you do need to verify factual claims more carefully than with Claude.” It scored just 46% on WeirdML versus GPT-5.2’s 72%, indicating particular weakness on novel reasoning patterns.
MiniMax M2.5 falls in between — 36th percentile on Benchable.ai’s hallucination benchmark. More troublingly, Slinn documented it “moving the goalposts” by changing linter thresholds to make errors disappear rather than fixing underlying code. That kind of behaviour is particularly dangerous in automated pipelines where you might not catch the dodge.
For production work, the reliability hierarchy is clear: Opus 4.6 > GLM-5 >> MiniMax M2.5 > Kimi K2.5.
Pricing in practice
Per-token pricing doesn’t tell the whole story because these models differ dramatically in verbosity.
| Model | Output $/1M tokens | Typical tokens per task | Effective task cost |
|---|---|---|---|
| MiniMax M2.5 | $1.20 | ~17M (median) | Lowest |
| GLM-5 | $3.20 | ~110M (6.5× median) | Moderate |
| Kimi K2.5 | $3.00 | ~89M (6× median) | Moderate-high |
| Opus 4.6 | $25.00 | ~17M (median) | Highest |
GLM-5 generated 110M output tokens on one benchmark evaluation where the median was 17M — a 6.5× multiplier that erodes its per-token advantage. Kimi K2.5 is similarly verbose at 6× average. M2.5 is the most token-efficient of the three, making its pricing advantage even more pronounced in practice.
All three offer subscription coding plans designed for agentic tool integration: GLM’s Coding Plan runs $3–30/month, Kimi’s API requires a minimum $1 recharge with a $19/month membership tier, and MiniMax’s Coding Plan is $10–50/month. OpenRouter availability is excellent for all three — 13–17 providers each, with free tiers available. No geo-restrictions or Chinese phone number requirements.
For self-hosting: M2.5 is the only realistic option on consumer hardware at ~101GB for a 3-bit GGUF quantisation, fitting a 128GB unified-memory Mac at 20–25 tok/s. GLM-5 requires 8× H100/H200 GPUs. Kimi K2.5 needs 4× H200 minimum.
The routing strategy
The practical answer isn’t picking one model. It’s routing tasks to the right model.
MiniMax M2.5 as your daily driver. Its SWE-Bench score, spec-first planning behaviour, and industry-leading tool calling make it the strongest candidate for routine backend work — API endpoints, migrations, service classes, test generation. At 20× cheaper than Opus on output tokens, the savings on high-volume agentic loops add up fast. Configure it as your Opus-tier replacement with a cheaper model handling the Sonnet/Haiku tiers.
GLM-5 as your reliability backstop. When you need low hallucination — complex query logic, security-sensitive middleware, anything where a confident wrong answer is worse than a slow right answer — GLM-5’s factual precision is unmatched among open models. Its thoroughness also makes it excellent for comprehensive test suites. Accept the 2× speed penalty for tasks where correctness outweighs velocity.
Kimi K2.5 for frontend and visual work. When you’ve got mockups or screenshots to convert into components, K2.5’s visual debugging loop — rendering output, comparing to the design, spotting CSS issues, fixing autonomously — is genuinely useful. But verify its factual claims aggressively and watch for verbosity blowing out your token budget.
Opus 4.6 for the hardest 20%. Large refactors spanning dozens of files, architectural decisions about service boundaries, ambiguous product specs requiring judgement, and any autonomous session where a mistake has significant downstream consequences. As one r/LocalLLaMA developer put it: “The Chinese models are improving rapidly. A year from now, this comparison might look completely different. But for now, I’d use them as secondary assistants for quick tasks and stick with Claude for real work.”
That assessment is slightly too conservative — M2.5 has earned primary-model trust for well-defined tasks. But the underlying caution about complex, high-stakes work remains sound. Route intelligently, review carefully, and keep watching. GLM-5-Turbo just dropped on OpenRouter, MiniMax M2.7 has been spotted in documentation, and Kimi’s team hinted at a smaller 200–300B model for local deployment. This comparison has a shelf life measured in weeks.