OpenCode Go: Can $10/Month Open Models Replace Frontier APIs?

The short answer is: for most coding tasks, yes.

OpenCode Go is a $10/month subscription ($5 for the first month) that gives you a single API key to 12 curated open coding models hosted in the US, EU, and Singapore with a zero-data-retention policy. The included monthly usage is $60 — that is 6x leverage before any overage.

Want to try it? Sign up with my referral link — we both get a $5 usage credit.

The economics alone are interesting. But what makes this genuinely relevant is that the models themselves have caught up to closed frontiers on the benchmarks that actually matter for production coding.

What OpenCode Go actually is

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OpenCode Go handles model-provider benchmarking, routing, and access negotiation. One API key. One predictable monthly bill. No juggling multiple provider accounts or per-token pricing.

Subscription terms:

Overage draws from your OpenCode Zen balance if enabled.

The $60 monthly cap means you can burn through a lot of tokens if you pick cheap models, for instance DeepSeek V4 Flash gets you ~31,650 requests per 5-hour window, MiMo-V2.5 gets you ~30,100, while Kimi K2.6 gets you ~1,150.

A note on “requests” vs tokens

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OpenCode Go bills in monetary limits ($12/5h, $60/month), but their documentation talks about requests per window rather than per-token rates. This makes sense for their product but can be confusing if you are used to thinking in tokens.

One request is one API call — you send a prompt, the model generates a response. But a “request” is not a fixed-size unit.

A request against DeepSeek V4 Flash averages ~790 input tokens, ~68K cached context tokens, and ~280 output tokens. A request against Kimi K2.6 or GLM-5.1? ~870 input, ~55K cache, ~200 output and ~700 input, ~52K cache, ~150 output respectively — similar input sizes, but the reasoning models generate longer chains of thought, which means more output tokens per request.

So when you see “31,650 requests per 5h” for V4 Flash, that number reflects OpenCode’s observed average request size. A reasoning model consumes more budget per call because it generates more tokens per request — even if the per-token rates look similar. Treat request-per-window numbers as order-of-magnitude guides, not guarantees.

How Go models compare to frontier models

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Before diving into individual models, here is the headline comparison against the four major closed frontier models as of May 2026.

SWE-bench scores are a mix of vendor-reported and independently-verified results. “Pro” is the harder benchmark — multi-language, larger repos. Pricing reflects representative public API pricing at the time of writing, not OpenCode Go billing, which is listed separately below.

The pattern is clear: several Go models land within a few points of frontier closed models on SWE-bench Verified at far lower prices. On SWE-bench Pro, Qwen3.7 Max (60.6%) leads the Go lineup ahead of Kimi K2.6 (58.6%) and GLM-5.1 (58.4%), though all three remain behind the strongest published Claude Opus 4.7 and GPT-5.5 results.

The 12 Go models, practically

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Not all Go models are created equal. The lineup spans from 10B-active-parameter efficiency beasts to 1.6T frontier chasers. Here is what actually matters for each.

Tier 1: Maximum agentic coding quality

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Kimi K2.6 (Moonshot AI, April 2026)

The most capable model in Go for agentic coding by several metrics. 1T total / 32B active MoE, 256K context. Scores 80.2% SWE-bench Verified and 58.6% SWE-bench Pro — ahead of GPT-5.4 (the predecessor to GPT-5.5) on the hard benchmark and competitive with the best closed-model results. Its Agent Swarm system deploys up to 300 sub-agents coordinating 4,000 steps on a single task.

On April 30, 2026, K2.6 finished first in Day 12 of the AI Coding Contest (Word Gem Puzzle) ahead of GPT-5.5 (3rd) and Claude Opus 4.7 (5th), according to contest organizer Rohana Rezel (source). Available across 11 API providers at the time of writing.

• AA Intelligence Index: 54 (among the highest of any open-weight model)
• Supported context: 256K
• Go pricing: $0.95 input / $4.00 output per 1M tokens
• Go requests per 5h: ~1,150
• ~99 t/s on Kimi API

DeepSeek V4 Pro (DeepSeek, April 2026)

1.6T / 49B active, 1M context. The biggest model in Go. Leads all models (open or closed) on LiveCodeBench Pass@1 at 93.5% and Codeforces at 3,206 — the highest competitive-programming rating publicly reported for a model at release.

SWE-bench Verified scores 80.6%, and Terminal-Bench 2.0 reaches 67.9% with a 1M-token context window. Its hybrid CSA+HCA attention slashes compute: only 27% of the FLOPs and 10% of the KV cache of V3.2 at 1M context. That’s how a 1.6T model stays economically viable.

• AA Intelligence Index: ~52–57 (varies by source/providers)
• Supported context: 1M
• Go pricing: bundled in plan
• Go requests per 5h: ~3,450
• ~30–60 t/s depending on reasoning mode (non-reasoning faster)

Qwen3.7 Max (Alibaba, May 2026)

Newest addition to the Go lineup, replacing Qwen3.5 Plus. Significantly more capable but also more expensive — $2.50 input / $7.50 output per 1M tokens, making it the priciest model in Go. 950 requests per 5h is the second-lowest in the lineup (ahead of only GLM-5.1). Positioned as the premium large-context option for tasks where quality justifies the cost.

The benchmark story is about agentic execution, not just chat polish. The standout score is 60.6% on SWE-bench Pro — the highest of any model in the Go lineup (ahead of Kimi K2.6 at 58.6% and GLM-5.1 at 58.4%). On SWE-bench Verified, it scores 80.4%, essentially matching DeepSeek V4 Pro (80.6%) and Gemini 3.1 Pro (80.6%). Terminal-Bench 2.0-Terminus reaches 69.7%, topping every Go model except GPT-5.5 (82.7%).

Qwen emphasizes that these were run with an internal agent scaffold using bash and file-edit tools — closer to real agent operation than single-turn coding prompts. Also notable: a reported 35-hour autonomous kernel optimization run on unseen T-Head ZW-M890 hardware, reaching 10.0x geometric mean speedup through 1,158 tool calls.

• SWE-bench Verified: 80.4%
• SWE-bench Pro: 60.6% (highest in Go)
• Terminal-Bench 2.0: 69.7%
• GPQA-Diamond: 92.4%
• MCP-Mark: 60.8%
• BFCL-V4: 75.0%
• Go pricing: $2.50 input / $7.50 output per 1M tokens
• Go requests per 5h: ~950
• Context: 1M

Verdict

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K2.6 for agent swarm capability. V4 Pro for competitive programming, LiveCodeBench, or 1M-context tasks that need top-end open-model quality. Qwen3.7 Max for the strongest SWE-bench Pro score in Go (60.6%), if you can afford the throughput trade-off. The choice depends on which benchmark matches your workload.

Tier 2: Best value workhorses

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MiniMax M2.5 (MiniMax, February 2026)

230B / 10B active MoE, 205K context. At its standard API price of $0.15/M input and $1.20/M output, it scores 80.2% on SWE-bench Verified while remaining dramatically cheaper than frontier closed models. Runs at ~100 t/s, completing evaluations 37% faster than its predecessor.

Leads Multi-SWE-Bench (multilingual, 10+ languages) at 51.3%, suggesting it generalizes beyond English codebases. For high-volume agentic coding workflows where cost is the primary constraint, M2.5 is hard to beat.

• AA Intelligence Index: 42 (lower composite, but coding-specific evals are strong)
• Go pricing: $0.30 input / $1.20 output / $0.06 cache per 1M tokens
• Go requests per 5h: ~6,300
• ~100 t/s

DeepSeek V4 Flash (DeepSeek, April 2026)

284B / 13B active, 1M context. The lightweight sibling to V4 Pro, released the same day. $0.14/M input, $0.28/M output — 12.4x cheaper per output token than V4 Pro — while trailing it by only 1.6 points on SWE-bench Verified (79.0% vs 80.6%).

The Go request volume is staggering: 31,650 requests per 5-hour window. For the 70–80% of tasks that are code review, RAG, single-function refactors, and debugging, Flash performance is sufficient. Use Flash for the bulk of work, escalate to Pro or K2.6 for hard cases.

• AA Intelligence Index: 47
• Go requests per 5h: 31,650 (highest in Go, closely followed by MiMo-V2.5 at 30,100)
• ~97 t/s, 1.18s TTFT

Verdict

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MiniMax M2.5 for cost-per-benchmark-point. DeepSeek V4 Flash for raw volume and as the default workhorse. MiMo-V2.5 as a surprisingly cheap alternative with 30,100 req/5h. Qwen3.7 Max for premium large-context tasks where you need the best Qwen quality.

Tier 3: Long-horizon autonomous tasks

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MiMo-V2.5-Pro (Xiaomi, April 2026)

1.02T / 42B active, 1M context. Purpose-built for hours-long unsupervised coding sessions. Xiaomi reports it built a complete compiler in 4.3 hours and a desktop video editor (~8,000 lines) in 11.5 hours using ~1,870 tool calls.

Headline metric: Terminal-Bench 2.0 at 68.4% with SWE-bench Pro at 57.2%. Pure text model — no vision or audio input.

• AA Intelligence Index: 54 (tied with K2.6)
• Go pricing: $1.74 input / $3.48 output per 1M tokens
• Go requests per 5h: ~3,250
• ~57–70 t/s

MiMo-V2.5 (Xiaomi, April 2026)

Base variant without the Pro long-horizon optimizations. Same 1.02T / 42B architecture but tuned for lower latency and higher throughput. At $0.14 input / $0.28 output per 1M tokens (same as DeepSeek V4 Flash), it delivers a massive 30,100 requests per 5h on Go — nearly matching V4 Flash for volume while offering the MiMo architecture. A strong alternative workhorse if you want the MiMo family without the Pro autonomous-session premium.

GLM-5.1 (Z.AI, April 2026)

754B / 40B active, 203K context. At release, Z.AI reported 58.4% on SWE-bench Pro, ahead of GPT-5.4 (57.7%), the predecessor to GPT-5.5. Z.AI’s benchmarks (shared on their official model page) also report 8+ hour autonomous runs, including building a complete Linux desktop system from scratch across 655 iterations with no human intervention.

Notable: generates more tokens than peers to reach equivalent answers (verbose). Benchmarks are vendor-reported; independent verification may show more modest results.

• AA Intelligence Index: 51
• Go requests per 5h: ~880
• ~55–59 t/s, 1.42s TTFT

GLM-5 (Z.AI, February 2026)

Predecessor to GLM-5.1. Same 754B / 40B active architecture, lower benchmark scores, but higher throughput on Go — 1,150 requests per 5h vs GLM-5.1’s 880. Relevant if you need the GLM family on a budget and the 5.1-level improvements aren’t critical.

Verdict:

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MiMo-V2.5-Pro for Terminal-Bench-heavy workloads, multimodal needs, or 1M context. Qwen3.7 Max if SWE-bench Pro leadership matters (60.6% vs GLM-5.1 at 58.4%), and you can tolerate the low throughput. GLM-5.1 if you want long-autonomy runs and are willing to trade verbosity for task completion depth.

Tier 4: Specialized picks

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Kimi K2.5 (Moonshot AI, January 2026)

The predecessor to K2.6. Still in Go, slightly cheaper. Its Agent Swarm (100 sub-agents, 1,500 steps) makes it interesting for complex search and research tasks. Moonshot reports BrowseComp at 78.4% and HLE at 50.2% (ahead of Claude Opus 4.5 on the Hard benchmark), which points to genuine reasoning depth.

But it is slow (44 t/s, 2.89s TTFT) and verbose. K2.6 is better in almost every dimension. Use K2.5 only if you need the specific Agent Swarm version or have a tight budget (yields more requests per window than K2.6 in Go).

Qwen3.6 Plus (Alibaba, April 2026)

Update to Qwen3.5 Plus with significantly better agentic coding and tool-use. 78.8% SWE-bench Verified, 61.6% Terminal-Bench 2.0 (surpasses Claude Sonnet 4.6). 1M context, “Auto” mode for adaptive web search and code interpreter invocation. Sits below Qwen3.7 Max in the Qwen family — cheaper and with higher throughput (3,300 req/5h vs 950), making it the better value pick if Qwen Max-level quality isn’t needed.

MiniMax M2.7 (MiniMax, March 2026)

Self-evolving successor to M2.5. Same architecture, same output price ($1.20/M), but slower and with stronger agentic capabilities. SWE-bench Pro improves from 55.4% to 56.2%. If you specifically need its agentic improvements over M2.5, it’s worth the trade-off.

Pricing and request volume

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The Go plan uses monetary usage limits. Cheaper models get more requests.

Go bills in monetary usage limits, not per-token rates. The per-token figures shown here are effective rates reverse-engineered from typical request patterns and observed token consumption, not official itemized pricing. “Bundled” means per-token rates are not publicly itemized by OpenCode for that model — pricing draws directly from the $60/month usage allotment. Request estimates are based on OpenCode’s observed average token patterns per model family. A “request” is one API call, but the size varies significantly: a reasoning model like GLM-5.1 or Kimi K2.6 generates far more output tokens per request than a lightweight model like V4 Flash, which is why their per-window request counts are much lower.

Where frontier still leads

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Being fair to the closed models: they still have real advantages.

GPT-5.5 leads on Terminal-Bench 2.0 (82.7% vs the best Go model at 68.4%) and the AA Intelligence Index (60 vs 54–57 for the best open model, depending on source). For tasks where every point of benchmark improvement matters directly (like competitive programming finals), GPT-5.5 is still the ceiling.

Claude Opus 4.7 still leads most of the published closed-model comparisons in this article, including SWE-bench Pro, and it remains particularly strong on reasoning depth for nuanced multi-step tasks, HLE-style benchmarks, and output quality for long trajectories. Its AA Intelligence Index of 57 reflects this.

Gemini 3.1 Pro at $2.00/M input and $12.00/M output is more competitive on price than the other frontier models, and its GPQA-Diamond (94.3%) and ARC-AGI-2 scores are unmatched by any Go model.

First-token latency is another area where frontier APIs tend to be more consistent. OpenCode routes through third-party providers, and latency varies. Public independent benchmarks for Go endpoint latency do not exist at the same quality as provider-direct data. For interactive pair-programming, this matters.

Practical recommendations

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Maximum coding quality → Kimi K2.6 or DeepSeek V4 Pro
K2.6 for agent swarm and multi-agent coordination. V4 Pro for competitive programming and LiveCodeBench. Qwen3.7 Max when you need the deepest reasoning on the hardest software engineering tasks.

Best cost-per-task / high-volume workhorse → DeepSeek V4 Flash or MiMo-V2.5
Both deliver massive request volume at the lowest per-token rates in Go. Use Flash for raw throughput and benchmark scores; MiMo-V2.5 if you want the MiMo architecture, multimodal input, or 1M context at budget pricing.

Long-horizon autonomy → MiMo-V2.5-Pro
Built for multi-hour unsupervised sessions. 1M context. Text only — no vision or audio.

1M context window → MiMo-V2.5(-Pro), Qwen3.7 Max, or DeepSeek V4 Flash.
Kimi and GLM are limited to 200–256K. MiniMax to 205K. If you need to ingest an entire large codebase in one request, the 1M-context models are your only option within Go.

Bottom line

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OpenCode Go at $10/month removes two barriers to using open models: procurement overhead (one API key, one bill) and infrastructure (curated hosting across global regions).

The bigger shift is economic. The models in this lineup are now good enough that the question is no longer “Can open models match frontier?” but “Which model is the right tool for this specific task?” Not every coding job needs the most expensive frontier API. A $30/M output model is overkill for a code review. A lightweight model at $0.28/M output handles routine refactors, RAG, and debugging just fine.

The real skill is learning to route. For most workflows, the optimal setup is a hybrid: cheap open models for the bulk of work — code review, refactors, RAG, debugging — and frontier models reserved for the narrow slice where every point of benchmark quality or millisecond of latency matters. This is not about replacing closed APIs with open ones. It is about using the right tool for each layer of the stack.

For most teams, the practical setup is a tiered router: a cheap workhorse as the default, a premium open model for hard tasks, and a frontier model reserved for final validation or cases where every point of quality matters.

I’ve been using OpenCode Go daily for months. The workflow that works is a deliberate mix: open models for volume and routine work, frontier models for the final layer where quality matters most. Flash or MiMo-V2.5 for the bulk, Qwen3.7 Max or Kimi K2.6 for the hard parts, Claude or GPT for validation. The savings are real, and the quality gap for everyday tasks is smaller than the pricing gap suggests.

Worst case? You’re out $10 and know exactly why open models aren’t ready for your stack. Best case? You build a hybrid pipeline that costs a fraction of a frontier-only setup without sacrificing the tasks that actually need the frontier.

This article was written with an AI agent at my side — I brought the expertise, it helped with the words.

Sources

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• OpenCode Go documentation — model lineup, pricing, request counts (May 2026)
• OpenCode Go referral link — $5 credit for you and me
• Qwen3.7-Max benchmark page — SWE-bench Pro, Terminal-Bench, and agentic coding scores (May 2026)
• Artificial Analysis — intelligence index and independent benchmarks
• OpenRouter — model availability and pricing reference
• Official model pages: Z.AI (GLM), Moonshot AI (Kimi), Xiaomi (MiMo), MiniMax (MiniMax), Alibaba (Qwen), DeepSeek (DeepSeek)
• Frontier comparisons: Anthropic (Claude), OpenAI (GPT), Google DeepMind (Gemini)

Benchmark scores are a mix of vendor-reported and independently-verified results. SWE-bench Pro and Terminal-Bench 2.0 are newer benchmarks; always check methodology before making production decisions.