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Qwen3-Coder alternatives

7 tools we tested head to head against Qwen3-Coder, ranked — and what each one actually does differently.

last reviewed 29 jul 2026 · from our best 8 coding llms ·list curated by Onur Ozcanxin

first — what you'd be leaving

Qwen3-Coder ranks #1 of 8 in our coding llms testing. apache 2.0 confirmed in the licence text, a million tokens of context, and now a version small enough to actually run..

90/100

the cleanest licence and the longest context in the category, with a february variant that finally makes it deployable outside a datacentre.

why people look for an alternative
  • flagship needs ~240gb at 4-bit — server only
  • swe-bench verified figures conflict across sources
  • fill-in-the-middle support unconfirmed for this generation
  • two very different models under one name

stay with Qwen3-Coder if plain apache 2.0, verified in the licence file is the thing you care about most — nothing below beats it on that.

the short version
best alternativeDeepSeek Coderfrontier-tier coding where the score matters more than running it yourself.88/100
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  1. 1

    DeepSeek Coder

    #2 in coding llms · the brand is now an api alias — and the models behind it went from a restrictive weights licence to plain mit.

    88/100

    verdictthe strongest swe-bench numbers here under a licence that got more permissive rather than less — with no small variant left for anyone who wanted the original.

    DeepSeek Coder vs Qwen3-Coder
     Qwen3-CoderDeepSeek Coder
    pricefree (apache 2.0)free (mit)
    free tieryesyes
    licenceapache 2.0mit
    params480b/35b active; next 80b/3b284b/13b active (flash)
    context262k, 1m via yarnnot confirmed per variant
    commercial useunrestrictedunrestricted
    runs on~240gb, or ~40gb for next~140gb at 4-bit

    switch forfrontier-tier coding where the score matters more than running it yourself.

    pros
    • +plain mit on both code and weights, a genuine loosening
    • +80.6% swe-bench verified on v4-pro
    • +frontier-tier coding without a licence negotiation
    • +one-million-token-scale context in the v4 family
    cons
    • no standalone coder models remain — it's an api alias
    • smallest option needs ~140gb at 4-bit
    • nothing runs locally, unlike the original 1.3b-33b line
    • fill-in-the-middle support unconfirmed for v4
  2. 2

    Seed-Coder-8B

    #3 in coding llms · plain mit, native fill-in-the-middle, and it fits in five gigabytes — the practical local pick.

    84/100

    verdictthe best thing here that actually runs on a laptop, with the cleanest licence and the fill-in-the-middle training the frontier models skipped.

    Seed-Coder-8B vs Qwen3-Coder
     Qwen3-CoderSeed-Coder-8B
    pricefree (apache 2.0)free (mit)
    free tieryesyes
    licenceapache 2.0mit
    params480b/35b active; next 80b/3b8b
    context262k, 1m via yarn32k tokens
    commercial useunrestrictedunrestricted
    runs on~240gb, or ~40gb for next~5gb at 4-bit

    switch foreditor completion on your own machine, where latency beats leaderboard position.

    pros
    • +plain mit with no conditions
    • +native fill-in-the-middle and spm training
    • +~5gb at 4-bit — genuinely local
    • +humaneval 84.8 and mbpp 85.2 self-reported
    cons
    • 8b is the only size — no larger variant
    • no update since june 2025
    • 32k context, shortest among the current models here
    • cannot match frontier agentic coders on large repos
  3. 3

    Granite (IBM)

    #4 in coding llms · apache 2.0, native fim, half a million tokens of context — and the dedicated code line is officially deprecated.

    80/100

    verdictthe cleanest licensing story in this ranking and native fim at 5gb — on a product line ibm has folded into its general models.

    Granite (IBM) vs Qwen3-Coder
     Qwen3-CoderGranite (IBM)
    pricefree (apache 2.0)free (apache 2.0)
    free tieryesyes
    licenceapache 2.0apache 2.0
    params480b/35b active; next 80b/3b3b, 8b, 30b dense
    context262k, 1m via yarn512k in 4.1
    commercial useunrestrictedunrestricted
    runs on~240gb, or ~40gb for next~5gb at 4-bit (8b)

    switch forenterprises wanting unambiguous licensing and very long context at sizes that run locally.

    pros
    • +apache 2.0 with licence and documentation in agreement
    • +cryptographically signed weights
    • +native fill-in-the-middle support
    • +512k context in granite 4.1 at ~5gb for the 8b
    cons
    • dedicated granite code line explicitly deprecated
    • coding is now a general-model capability, not a specialism
    • trails dedicated coders on pure code benchmarks
    • legacy code models capped at 4k context
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  4. 4

    Devstral

    #5 in coding llms · the small one is apache 2.0 and the big one stops working for you the month you pass $20m in revenue.

    77/100

    verdicttwo models, two licences, and mistral says so plainly — which after codestral from the same company is a meaningful improvement in candour.

    Devstral vs Qwen3-Coder
     Qwen3-CoderDevstral
    pricefree (apache 2.0)free below the threshold
    free tieryesyes
    licenceapache 2.0modified mit (123b), apache (24b)
    params480b/35b active; next 80b/3b123b and 24b
    context262k, 1m via yarn128k on small
    commercial useunrestrictedcapped at $20m/month on 123b
    runs on~240gb, or ~40gb for next~14gb (24b), ~70gb (123b)

    switch foragentic coding scaffolds, at 24b for anyone, at 123b for companies under the revenue line.

    pros
    • +devstral small 24b is plain apache 2.0
    • +mistral states the licence split explicitly
    • +purpose-built for agentic coding scaffolds
    • +24b runs in about 14gb at 4-bit
    cons
    • 123b rights revoked above $20m monthly revenue
    • hard cutoff rather than a graduated licence fee
    • fill-in-the-middle support unconfirmed
    • devstral 2's context length not confirmed
  5. 5

    GLM-4.6

    #6 in coding llms · near-frontier coding under what its badge says is mit — and the licence file itself returns 404.

    72/100

    verdictreportedly close to claude sonnet 4 on real-world coding, with the one thing this ranking insists on — a readable licence — missing.

    GLM-4.6 vs Qwen3-Coder
     Qwen3-CoderGLM-4.6
    pricefree (apache 2.0)free (mit, unverified)
    free tieryesyes
    licenceapache 2.0mit per tag, file unreadable
    params480b/35b active; next 80b/3b~355b
    context262k, 1m via yarn200k tokens
    commercial useunrestrictedunrestricted, unverified
    runs on~240gb, or ~40gb for next~180gb at 4-bit

    switch forhosted agentic coding through claude code or cline-style tooling, where you won't be self-hosting anyway.

    pros
    • +reported near-parity with claude sonnet 4 on cc-bench
    • +200k context, up from 128k in glm-4.5
    • +built for claude code and cline-style integration
    • +mit per the hugging face licence tag
    cons
    • raw licence file returned 404 — text unverified
    • ~180gb at 4-bit, datacentre only
    • superseded by glm-5.1 and 5.2 as general flagships
    • no primary benchmark figure retrievable from zhipu
  6. 6

    StarCoder2

    #7 in coding llms · 600 languages at two gigabytes, under a licence whose conditions you have to pass on to your own users.

    66/100

    verdictstill the best small multi-language coder and two and a half years old — with commercial permission that comes with strings attached to your own terms of service.

    StarCoder2 vs Qwen3-Coder
     Qwen3-CoderStarCoder2
    pricefree (apache 2.0)free (openrail-m)
    free tieryesyes
    licenceapache 2.0bigcode openrail-m v1
    params480b/35b active; next 80b/3b3b, 7b, 15b
    context262k, 1m via yarn16k, 4k sliding window
    commercial useunrestrictedpermitted with use restrictions
    runs on~240gb, or ~40gb for next~2-9gb at 4-bit

    switch forbroad language coverage on small hardware, for teams willing to carry the licence terms downstream.

    pros
    • +600+ programming languages via the stack v2
    • +sizes from ~2gb, the smallest here
    • +native fill-in-the-middle training
    • +15b reported matching codellama-34b
    cons
    • openrail-m restrictions must be passed to your end users
    • disclosure obligation on public-facing generated content
    • no update since february 2024
    • 16k context with 4k sliding-window attention
  7. 7

    Codestral

    #8 in coding llms · downloadable, and forbidden in production — the licence is a flat non-commercial bar, not a threshold.

    58/100

    verdictthe most restrictive licence in this ranking by a distance, on a model whose current iteration may not have open weights at all.

    Codestral vs Qwen3-Coder
     Qwen3-CoderCodestral
    pricefree (apache 2.0)research use only
    free tieryesyes
    licenceapache 2.0mistral non-production licence
    params480b/35b active; next 80b/3b22b (open checkpoint)
    context262k, 1m via yarn128k tokens
    commercial useunrestrictedprohibited without a paid licence
    runs on~240gb, or ~40gb for next~13gb at 4-bit

    switch forresearch, evaluation and prototypes that will never ship.

    pros
    • +long-established fill-in-the-middle tuned for editors
    • +128k context on the documented model card
    • +22b runs in about 13gb for research use
    • +available as a supported paid api
    cons
    • non-production licence bars commercial use entirely
    • commercial terms require a private negotiation
    • current 25.08 iteration appears to be api-only
    • launch framing understates the prohibition

how these were compared

every tool on this page went through the same test as Qwen3-Coder — same tasks, same order, scored the same way. the comparison tables are the figures from that testing, not vendor spec sheets.

the coding llms test in full →
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