Searches for a “Claude Code alternative” usually start with one of two pains:
the subscription limits (the five-hour usage windows belong to the Claude
plan, not to the tool) or the bill (agentic coding burns tokens like
nothing else you run). Before comparing tools, it helps to split the question
in two, because they have different answers:
The harness — the terminal agent itself: the REPL, the tool-calling
loop, permissions, MCP support. Claude Code is one harness; there are now
several good ones.
The model — what actually writes the code, and what you’re actually
paying for.
You can swap either one independently. Here’s the honest map of both.
All of these are good software. If your pain is the harness itself — you want
an IDE sidebar, or a different permission model — pick from the table and
you’re done. But notice what the table also says: half of these tools don’t
come with a model at all. You bring an API key, and the model behind it is
where the cost and the quality actually live.
The option most people miss: keep Claude Code, swap the model
exportANTHROPIC_MODEL="kimi-k3"# or glm-5.2, deepseek-v4-flash, ...
Your muscle memory, your .claude/ config, your MCP servers, your slash
commands — everything stays. What changes is the engine and the meter: on a
CheapestInference pool, usage during your reserved time blocks is a flat
monthly fee, with no per-token billing and no five-hour windows.
If you’re otherwise happy on a Claude subscription and just want overnight
runs to survive the window resets, you don’t need to switch anything:
claude-auto-retry waits out the
limit and resumes the session for you. Free, one npm install.
The harnesses are mostly free and open-source (OpenCode, Aider, Cline, Qwen
Code, Codex CLI’s source). What’s never free at scale is the model behind
them — a serious agentic session runs millions of tokens, so the real
comparison is per-token bills vs. subscriptions vs. flat-rate blocks.
Yes. Claude Code works with any Anthropic-compatible endpoint via
ANTHROPIC_BASE_URL — no plugin, no proxy. That’s the
“keep the harness, swap the model” path above.
What’s the cheapest way to run a coding agent all day?
A flat-rate block: agentic coding is exactly the workload where per-token
billing hurts most, because the agent — not you — decides how many tokens to
spend. A pool subscription makes the
heavy week cost the same as the light one. For per-token numbers across the
market, see the live pricing comparison.
GLM-5.3 (API id glm-5.3) is Z.ai’s (Zhipu AI) new coding and agentic model, released today, August 14, 2026, under the tagline “Built to Code. Ready for Cyber Defense.” The architecture story is unusual and worth being precise about: GLM-5.3 keeps the same 743B-parameter base model as GLM 5.2 — every reported gain comes from scaled-up post-training alone. On Z.ai’s own benchmark suite that post-training buys a lot: it calls GLM-5.3 the strongest open-weights coding model it has measured, and reports a cyber-security capability that grew faster than the company anticipated.
And the question this blog exists to answer: GLM-5.3 is officially under review for our pools, as of today. We already serve GLM 5.2 in the Frontier Pool, so 5.3 enters the pipeline as the natural upgrade candidate for that slot. What gates the decision is not quality signals — it’s that the model is API-only today: open weights are promised roughly two weeks out, after Z.ai completes its own safety evaluation. Live status is always on our models-under-review page.
Same 743B base as GLM 5.2 — not a new pretrain; gains from extended post-training
Context window
Z.ai advertises a 1M-token variant (glm-5.3[1m], with context compaction); the standard-API spec is not yet published
Reasoning
Effort levels low / high / max — default max; thinking cannot be disabled on the direct API
Open weights
Promised ~2 weeks after launch, pending Z.ai’s safety evaluation — no weights, no license, no model card today
License
Unpublished. GLM 5.2 shipped MIT; that precedent does not automatically set 5.3’s terms
List price (API)
Not yet published — GLM 5.2 lists $1.40 in / $4.40 out per 1M as reference
Availability today
First-party API access from Z.ai only — no open weights, no third-party serving yet; works with Claude Code, OpenCode, and Codex via compatible endpoints
All numbers below are Z.ai’s own reported results — vendor-run, not yet independently reproduced, and the Artificial Analysis index hasn’t rated GLM-5.3 yet. With that caveat on the table, the GLM 5.2 → GLM-5.3 deltas are the story, because the base model is identical:
Benchmark
GLM 5.2
GLM-5.3
Δ
Terminal-Bench 2.1
81.0
88.2
+9%
Terminal-Bench 3.0
4.6
28.3
+515%
DeepSWE v1.1
46.2
66.9
+45%
SWE-Marathon v1.1
19.4
42.5
+119%
FrontierSWE
67.5
78.1
+16%
NL2Repo
48.9
58.0
+19%
Toolathlon Verified
59.9
73.0
+22%
AutomationBench v1.0.6
26.2
48.2
+84%
CyberGym
77.2
84.5
+9%
Two readings. The charitable one: the biggest jumps land on the newest, hardest agentic benchmarks (Terminal-Bench 3.0, SWE-Marathon) — exactly where post-training on agent trajectories should show up, and exactly the workloads coding agents run all day. The skeptical one: several of these benchmarks are new or Z.ai-adjacent, and until independent runs land, “strongest open-weights coding model” is a claim, not a fact. Both readings can wait two weeks — the open-weights release is when independent verification becomes possible.
The cyber-defense angle — and why the weights are two weeks out
The unusual part of this launch is that Z.ai leads with cyber security as a first-class capability, not a footnote. It reports GLM-5.3 at 84.5 on CyberGym — above its figures for Claude Mythos 5 (83.8) and GPT-5.6 Sol (83.6) — and says the model found thousands of real vulnerabilities across open-source projects during training. Z.ai’s framing is defensive: vulnerability detection at scale.
That capability is also the stated reason the weights aren’t out yet. Rather than shipping weights on day one — as it did with GLM 5.2 — Z.ai is running a staged release: API first, then open weights roughly two weeks after launch, once its own safety evaluation and hardening work is complete. Whatever you think of the trade-off, it’s a more deliberate open-weights process than the ecosystem norm, and it puts a concrete clock on the one thing our review is waiting for.
Because the base is unchanged, this isn’t a “new model vs old model” decision so much as a post-training upgrade — the same shape as DeepSeek’s V4-Flash-0731 build, which we upgraded in place in the Core Pool within days of release. If GLM-5.3’s weights land with a usable license and it passes our quality evaluation on real coding and agent workloads, the natural outcome is the same: the Frontier Pool’s GLM slot upgrades, and every existing subscription simply gets the better model.
So the honest status board:
Quality — vendor numbers are strong; our own evaluation on real agent workloads (both OpenAI and Anthropic endpoints, tool calling included) runs in parallel: ⏳ in progress.
Fit — a post-training upgrade of a model already serving Frontier Pool workloads: as clean as fit gets.
Licensing — ⏳ waiting on Z.ai’s open-weights release and license text, on the ~two-week clock Z.ai itself set.
Is there an unlimited GLM-5.3 API?
Not yet, from anyone. GLM-5.3 is API-only and first-party today. It is under review for our flat-rate unlimited pools as a Frontier Pool candidate; meanwhile, GLM 5.2 — same base model — is served unlimited on time-block subscriptions today.
Does GLM-5.3 have open weights?
Not yet. Z.ai has committed to releasing open weights roughly two weeks after the August 14 launch, once its internal safety evaluation of the model’s cyber capabilities is complete. No license text or model card has been published; GLM 5.2’s MIT license doesn’t automatically carry over.
How much does the GLM-5.3 API cost?
Per-token list pricing hasn’t been published — the closest reference is GLM 5.2’s list rate ($1.40 in / $4.40 out per 1M) until Z.ai posts 5.3 rates.
What is the difference between GLM-5.3 and GLM 5.2?
Same 743B base model — GLM-5.3 is extended post-training on top of it, targeting agentic coding, tool use, and cyber-security workloads. Z.ai reports large gains on agentic benchmarks (SWE-Marathon 19.4 → 42.5, Terminal-Bench 3.0 4.6 → 28.3); all numbers are vendor-run so far.
What is the context window of GLM-5.3?
Z.ai advertises 1M tokens via the glm-5.3[1m] variant with context compaction. The standard-API context spec hasn’t been published yet.
CheapestInference serves Kimi K3 and Qwen3.8 Max (Flagship Pool), GLM 5.2 and MiniMax M3 (Frontier Pool) and DeepSeek V4 Flash and MiMo v2.5 (Core Pool) through one OpenAI- and Anthropic-compatible API on unlimited time-block subscriptions. See the pools or get started.
Qwen3.8 Max (also written “Qwen 3.8 Max”; API id qwen3.8-max) is Alibaba’s flagship model, announced July 19: a 2.4-trillion-parameter system with a 1M-token context window, scoring 58 on the independent Artificial Analysis Intelligence Index (v4.1.1) — top-five territory, two points behind Kimi K3 (60), the current open-weights ceiling. As we chart below, that score at Qwen’s list price lands it on the price-vs-intelligence Pareto frontier — and knocks Claude Sonnet 5 off it.
Update, August 14, 2026: the review is over — Qwen3.8 Max is live in our Flagship Pool. Alibaba shipped the open-weight variant on August 13, the licensing gate we describe below lifted, and every Flagship subscriber can now call it with model id qwen3.8-max — unlimited, flat-rate, from $149/mo, next to Kimi K3. Setup, specs and examples: Qwen3.8 Max API — pricing, access & subscription.
Released August 13, 2026 — Qwen3.8-2.4T-A95B, the open variant of the Max
Vision
Yes — image input
List price (API)
$2 input / $6 output per 1M tokens (cached input from $0.25)
The benchmark picture: 58 on the Artificial Analysis index (v4.1.1) puts Qwen3.8 Max above every previous Qwen release and within two points of Kimi K3 (60). Independent benchmarking is ongoing; active-parameter counts and MoE configuration haven’t been published, so per-token compute cost can’t be derived yet.
The head-to-head everyone is asking for — the two highest-scoring models in the open(-ing) ecosystem, and on paper they’re complements rather than rivals:
K3 holds the intelligence crown and the agentic-coding pedigree; Qwen3.8 Max answers with 2.5× cheaper output at two index points’ distance, plus standard sampling controls. One pool serving both covers the two profiles that matter — peak agentic reasoning and tunable long-context breadth — which is exactly what the Flagship Pool now does.
Qwen3.8 Max on the price-vs-intelligence Pareto frontier
A model is on the frontier when nothing tracked is both smarter and cheaper. At index 58 for a $6.00/1M list output price, Qwen3.8 Max steps onto the frontier — five points above Claude Sonnet 5 at 40% lower list price — and pushes Sonnet 5 off it:
Served in a CheapestInference poolUnder reviewReference frontier modelsPareto frontier
Four of the five models on that frontier — V4-Flash-0731, GLM 5.2, Qwen3.8 Max and Kimi K3 — are served here on flat rate. The monthly-updated, full-field version of this chart (with cost-per-task data and edition history) lives in our LLM Pareto Frontier report.
When this review was published (August 4), Qwen3.8 Max had no open weights and no open license. Qwen’s open releases (3.5, 3.6) shipped under Apache 2.0; its Max tier had historically stayed closed — a tension the community debated openly since the preview shipped. Our review status was explicit that licensing, not quality, was the gate.
On August 13 Alibaba resolved it: Qwen3.8-2.4T-A95B — the open-weight variant of the Max — shipped on Hugging Face and ModelScope, followed on August 14 by the dense Qwen3.8-27B under Apache 2.0. The final scorecard:
Quality — reviewed on real agent workloads through both our OpenAI and Anthropic endpoints, including tool calling: ✅ passed.
Fit — a second flagship-class model with a different profile (hybrid reasoning, standard sampling, 1M context, vision) next to Kimi K3: ✅ strong.
Is there an unlimited Qwen3.8 Max API?
Yes — live since August 14, 2026: the CheapestInference Flagship Pool serves Qwen3.8 Max with no token caps during your reserved hours, from $149/month for a daily 8-hour block, on the same subscription as Kimi K3. Model id qwen3.8-max; setup on the model page. Seats are very limited.
Does Qwen3.8 Max have open weights?
Yes, as of August 13, 2026: Alibaba published Qwen3.8-2.4T-A95B, the open-weight variant of the Max, on Hugging Face and ModelScope — and the dense Qwen3.8-27B followed on August 14 under Apache 2.0.
How much does the Qwen3.8 Max API cost?
Per token, list price is $2.00 per 1M input tokens and $6.00 per 1M output, with cached input from $0.25 — a 100M-token month lands around $200–600 depending on cache-hit rate and output mix. The credit-based subscription plans we analyzed in Qwen coding plans, explained cap usage per 5-hour and 7-day windows. For heavy use, our flat-rate unlimited route starts at $149/month.
What is the context window of Qwen3.8 Max?
1M tokens, per Alibaba’s published spec for the model.
CheapestInference serves Kimi K3 and Qwen3.8 Max (Flagship Pool), GLM 5.2 and MiniMax M3 (Frontier Pool) and DeepSeek V4 Flash and MiMo v2.5 (Core Pool) through one OpenAI- and Anthropic-compatible API on unlimited time-block subscriptions. See the pools or get started.
On July 31, DeepSeek shipped V4-Flash-0731 — not a bigger model, a retrained one. Same 284B-total / 13B-active architecture, same 1M-token context, same price bracket — but re-post-trained for agent work, and the reported jump is unusual: DeepSeek says the new Flash now beats its own larger V4-Pro-Preview on every one of the nine agent benchmarks it publishes.
If you use the Core Pool, there is nothing to migrate: the model id is still deepseek-v4-flash, and requests already serve the new build. Unlimited, flat-rate access starts from $16.49/mo ($14.02/mo billed annually) — live pricing on /pools.
Same architecture, new post-training. This is DeepSeek’s own reported before/after (vendor harness — no independent verification of these specific numbers yet):
V4-Flash-0731V4-Flash preview (the previous build, April 2026)
Full vendor-stated table for the 0731 build, next to the April preview it replaces:
Benchmark
Flash-0731
Flash preview (Apr)
Terminal-Bench 2.1
82.7
61.8
Cybergym
76.7
38.7
Toolathlon-Verified
70.3
49.7
DSBench-FullStack†
68.7
37.0
DSBench-Hard†
59.6
25.8
DeepSWE
54.4
7.3
NL2Repo
54.2
39.4
Agents’ Last Exam
25.2
15.8
AutomationBench Public
25.1
10.8
† DeepSeek-internal test sets; the rest are public benchmarks.
The DeepSWE number is the striking one: the retrain multiplied the preview’s score by seven without touching the architecture. And like the April preview, the 0731 checkpoint is a genuine open-weights release: the weights are published under MIT at deepseek-ai/DeepSeek-V4-Flash-0731 (304B parameters on the repo — the 284B base plus a draft module).
The independent signal comes from Artificial Analysis, which measures Flash-0731 at 50 on its Intelligence Index — ten points above the previous Flash. Honest framing: it does not beat Claude Opus 5 (61) or GPT-5.6 Sol (59) — nothing near this price does. What it does is land within seven points of Kimi K3 (57, the highest open-weights score on the index) from the budget model of the field — with a reference per-token price of $0.28 per 1M output tokens, roughly 90× below Claude Opus 5’s $25:
Served in a CheapestInference poolReference frontier models
Same data as a table, with list output prices alongside for scale — each model at its best published configuration (max effort where the index reports one); the median across all models Artificial Analysis tracks is 17:
Model
AA Intelligence Index
List output $/1M
On CheapestInference
Claude Opus 5
61
$25.00
—
Claude Fable 5*
60
$50.00
—
GPT-5.6 Sol
59
$30.00
—
Kimi K3
57
$15.00
Flagship Pool
Claude Opus 4.8
56
$25.00
—
GLM 5.2
51
$4.40
Frontier Pool
DeepSeek V4-Flash-0731
50
$0.28
Core Pool
MiniMax M3
44
$1.20
Frontier Pool
* Yes, one point below Claude Opus 5, even though Anthropic positions Fable 5 above Opus in capability. The index scores what AA actually evaluates: Fable 5 is measured in its “Adaptive Reasoning, Max Effort, Opus 4.8 Fallback” serving configuration — the variant with Anthropic’s additional dual-use safeguards — while Opus 5 runs at plain max effort. Independent index, published configurations, taken as-is.
Four of the eight models on that chart are served here on flat-rate subscriptions — and the 0731 retrain just moved the cheapest pool of the three into frontier territory.
V4-Flash-0731 vs Claude Opus 4.8, GPT-5.6 Luna, and Gemini 3.6 Flash
Is DeepSeek V4-Flash-0731 better than Claude Opus? On raw capability, no — and we won’t pretend otherwise. The strongest competitor in DeepSeek’s own release table is Claude Opus 4.8, and Opus wins every one of the nine shared benchmarks. What the table actually shows is how little it wins by, against a model priced roughly 90× higher per output token at list:
Benchmark (DeepSeek’s release table, vendor-run)
V4-Flash-0731
Claude Opus 4.8
Terminal-Bench 2.1
82.7
85.0
Cybergym
76.7
83.1
Toolathlon-Verified
70.3
76.2
DSBench-FullStack†
68.7
71.6
DSBench-Hard†
59.6
71.7
DeepSWE
54.4
58.0
NL2Repo
54.2
69.7
Agents’ Last Exam
25.2
25.7
AutomationBench Public
25.1
27.2
Reference output price, per 1M tokens
$0.28
$25.00
On Agents’ Last Exam the gap is half a point — effective parity. On Terminal-Bench 2.1 it is 2.3 points. NL2Repo and the (internal) DSBench-Hard are where the distance stays wide.
Against its actual price peers, the independent picture flips. Per Artificial Analysis: Flash-0731 sits one point behind GPT-5.6 Luna (50 vs 51 at max effort) with a cost per task roughly 60% lower — even after OpenAI’s price cut — ties Gemini 3.6 Flash (50), and lands on AA’s Pareto frontier for Intelligence vs Cost per Task: at this intelligence level, nothing tracked is cheaper per task.
Here is that frontier drawn out — intelligence against list output price. A model is on the frontier when nothing tracked is both smarter and cheaper; everything below-right of the line pays more for less. The 0731 retrain moved Flash onto it, and pushed MiniMax M3 off:
Served in a CheapestInference poolReference frontier modelsPareto frontier
Three of the five models on that frontier — Flash-0731, GLM 5.2 and Kimi K3 — are served here on flat rate. This chart is a snapshot of the frontier’s cheap end; the monthly-updated, full-field version (with cost-per-task data and edition history) lives in our LLM Pareto Frontier report.
And on a flat-rate subscription the per-token column stops mattering altogether: a Core Pool block is the same price whether your agent burns one million tokens or one billion.
Start claude as usual — every request runs on Flash-0731 with no per-token meter. The same key works in Cline, Roo Code, Continue, and anything that accepts a custom OpenAI base URL (setup guides).
Agent workloads are exactly where per-token bills explode: an agent re-sends its growing context on every tool call, and iteration count — not task value — drives the invoice. A model that is suddenly much better at agent work makes that math worse per-token and better flat-rate: more capable loops, same fixed monthly price. We ran the full per-token vs. flat break-even math in Unlimited DeepSeek: what a flat monthly subscription changes — every row of that table just got more favorable, because the same subscription now serves a stronger model.
The trade-offs, as always: usage is unlimited in tokens during your reserved 8-hour blocks, each subscription runs one request at a time (fair use), and outside your blocks the key doesn’t serve. Full details: DeepSeek V4 Flash API docs · Plans & Limits.
CheapestInference serves Kimi K3 (Flagship Pool), GLM 5.2 and MiniMax M3 (Frontier Pool) and DeepSeek V4 Flash and MiMo v2.5 (Core Pool) through one OpenAI- and Anthropic-compatible API on unlimited time-block subscriptions. See the pools or get started.
Kimi K3 is live on CheapestInference: Moonshot’s flagship — scoring 60 on the Artificial Analysis Intelligence Index — the top open-weights score — served through an OpenAI- and Anthropic-compatible API with unlimited usage at a flat monthly price, from $149/mo (live availability — seats are very limited). This is the practical guide: get access, call it, wire it into your coding agent.
Per-token, from Moonshot — $3.00 per 1M input tokens ($0.30 cached) and $15.00 per 1M output. Ideal for evaluation and light use; expensive fast for agent workloads.
Kimi memberships — Moonshot’s own plans bundle K3 access with request quotas per 5-hour and weekly windows (new signups have been intermittently paused since launch).
Unlimited time-block subscription (this guide) — reserve one or more daily 8-hour blocks on the Flagship Pool and use K3 with no token caps during your hours. From $149/mo per block; all three blocks = 24/7.
messages=[{"role": "user", "content": "Refactor this function..."}],
)
print(response.choices[0].message.content)
K3 is a reasoning model, but reasoning is off by default — you get fast, direct answers. Turn it on per request with "thinking": {"type": "enabled"} or reasoning_effort (low / high / max — K3’s deepest tier). With reasoning on, give it a generous max_tokens so long answers don’t truncate mid-thought, and stream (stream: true) for responsive UIs.
exportANTHROPIC_AUTH_TOKEN="sk-..."# your subscriber key
exportANTHROPIC_MODEL="kimi-k3"
exportANTHROPIC_SMALL_FAST_MODEL="kimi-k3"
Start claude as usual — every request now runs on K3 with no per-token meter. The same key works in Cline, Roo Code, Continue, and any client that accepts a custom OpenAI base URL (setup guides).
At list price, a coding agent that burns 100M tokens a month on K3 costs roughly $200–400 per-token, depending on your cache-hit and output mix — and heavy agentic users go far beyond that. A Flagship block at $149/mo covers your working day; all three blocks cover 24/7. The trade-offs: during your reserved hours usage is truly unlimited, each subscription runs one request at a time (fair use), and outside your blocks the key doesn’t serve. Full pricing detail: Plans & Limits · Kimi K3 API docs.
Seats on the Flagship Pool are very limited — when a block sells out it’s gone until someone leaves. Check live availability →
CheapestInference serves Kimi K3 (Flagship Pool), GLM 5.2 and MiniMax M3 (Frontier Pool) and DeepSeek V4 Flash and MiMo v2.5 (Core Pool) through one OpenAI- and Anthropic-compatible API on unlimited time-block subscriptions. See the pools or get started.
